From 4400c7139d57b810bc5c2c9f5772606d0aaf1ed0 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 13 七月 2023 17:35:41 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/models/encoder/e_branchformer_encoder.py | 465 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 465 insertions(+), 0 deletions(-)
diff --git a/funasr/models/encoder/e_branchformer_encoder.py b/funasr/models/encoder/e_branchformer_encoder.py
new file mode 100644
index 0000000..14028ed
--- /dev/null
+++ b/funasr/models/encoder/e_branchformer_encoder.py
@@ -0,0 +1,465 @@
+# Copyright 2022 Kwangyoun Kim (ASAPP inc.)
+# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""E-Branchformer encoder definition.
+Reference:
+ Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan,
+ Prashant Sridhar, Kyu J. Han, Shinji Watanabe,
+ "E-Branchformer: Branchformer with Enhanced merging
+ for speech recognition," in SLT 2022.
+"""
+
+import logging
+from typing import List, Optional, Tuple
+
+import torch
+
+from funasr.models.ctc import CTC
+from funasr.models.encoder.abs_encoder import AbsEncoder
+from funasr.modules.cgmlp import ConvolutionalGatingMLP
+from funasr.modules.fastformer import FastSelfAttention
+from funasr.modules.nets_utils import get_activation, make_pad_mask
+from funasr.modules.attention import ( # noqa: H301
+ LegacyRelPositionMultiHeadedAttention,
+ MultiHeadedAttention,
+ RelPositionMultiHeadedAttention,
+)
+from funasr.modules.embedding import ( # noqa: H301
+ LegacyRelPositionalEncoding,
+ PositionalEncoding,
+ RelPositionalEncoding,
+ ScaledPositionalEncoding,
+)
+from funasr.modules.layer_norm import LayerNorm
+from funasr.modules.positionwise_feed_forward import (
+ PositionwiseFeedForward,
+)
+from funasr.modules.repeat import repeat
+from funasr.modules.subsampling import (
+ Conv2dSubsampling,
+ Conv2dSubsampling2,
+ Conv2dSubsampling6,
+ Conv2dSubsampling8,
+ TooShortUttError,
+ check_short_utt,
+)
+
+
+class EBranchformerEncoderLayer(torch.nn.Module):
+ """E-Branchformer encoder layer module.
+
+ Args:
+ size (int): model dimension
+ attn: standard self-attention or efficient attention
+ cgmlp: ConvolutionalGatingMLP
+ feed_forward: feed-forward module, optional
+ feed_forward: macaron-style feed-forward module, optional
+ dropout_rate (float): dropout probability
+ merge_conv_kernel (int): kernel size of the depth-wise conv in merge module
+ """
+
+ def __init__(
+ self,
+ size: int,
+ attn: torch.nn.Module,
+ cgmlp: torch.nn.Module,
+ feed_forward: Optional[torch.nn.Module],
+ feed_forward_macaron: Optional[torch.nn.Module],
+ dropout_rate: float,
+ merge_conv_kernel: int = 3,
+ ):
+ super().__init__()
+
+ self.size = size
+ self.attn = attn
+ self.cgmlp = cgmlp
+
+ self.feed_forward = feed_forward
+ self.feed_forward_macaron = feed_forward_macaron
+ self.ff_scale = 1.0
+ if self.feed_forward is not None:
+ self.norm_ff = LayerNorm(size)
+ if self.feed_forward_macaron is not None:
+ self.ff_scale = 0.5
+ self.norm_ff_macaron = LayerNorm(size)
+
+ self.norm_mha = LayerNorm(size) # for the MHA module
+ self.norm_mlp = LayerNorm(size) # for the MLP module
+ self.norm_final = LayerNorm(size) # for the final output of the block
+
+ self.dropout = torch.nn.Dropout(dropout_rate)
+
+ self.depthwise_conv_fusion = torch.nn.Conv1d(
+ size + size,
+ size + size,
+ kernel_size=merge_conv_kernel,
+ stride=1,
+ padding=(merge_conv_kernel - 1) // 2,
+ groups=size + size,
+ bias=True,
+ )
+ self.merge_proj = torch.nn.Linear(size + size, size)
+
+ def forward(self, x_input, mask, cache=None):
+ """Compute encoded features.
+
+ Args:
+ x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
+ - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
+ - w/o pos emb: Tensor (#batch, time, size).
+ mask (torch.Tensor): Mask tensor for the input (#batch, 1, 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).
+ """
+
+ if cache is not None:
+ raise NotImplementedError("cache is not None, which is not tested")
+
+ if isinstance(x_input, tuple):
+ x, pos_emb = x_input[0], x_input[1]
+ else:
+ x, pos_emb = x_input, None
+
+ if self.feed_forward_macaron is not None:
+ residual = x
+ x = self.norm_ff_macaron(x)
+ x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
+
+ # Two branches
+ x1 = x
+ x2 = x
+
+ # Branch 1: multi-headed attention module
+ x1 = self.norm_mha(x1)
+
+ if isinstance(self.attn, FastSelfAttention):
+ x_att = self.attn(x1, mask)
+ else:
+ if pos_emb is not None:
+ x_att = self.attn(x1, x1, x1, pos_emb, mask)
+ else:
+ x_att = self.attn(x1, x1, x1, mask)
+
+ x1 = self.dropout(x_att)
+
+ # Branch 2: convolutional gating mlp
+ x2 = self.norm_mlp(x2)
+
+ if pos_emb is not None:
+ x2 = (x2, pos_emb)
+ x2 = self.cgmlp(x2, mask)
+ if isinstance(x2, tuple):
+ x2 = x2[0]
+
+ x2 = self.dropout(x2)
+
+ # Merge two branches
+ x_concat = torch.cat([x1, x2], dim=-1)
+ x_tmp = x_concat.transpose(1, 2)
+ x_tmp = self.depthwise_conv_fusion(x_tmp)
+ x_tmp = x_tmp.transpose(1, 2)
+ x = x + self.dropout(self.merge_proj(x_concat + x_tmp))
+
+ if self.feed_forward is not None:
+ # feed forward module
+ residual = x
+ x = self.norm_ff(x)
+ x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+
+ x = self.norm_final(x)
+
+ if pos_emb is not None:
+ return (x, pos_emb), mask
+
+ return x, mask
+
+
+class EBranchformerEncoder(AbsEncoder):
+ """E-Branchformer encoder module."""
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ attention_heads: int = 4,
+ attention_layer_type: str = "rel_selfattn",
+ pos_enc_layer_type: str = "rel_pos",
+ rel_pos_type: str = "latest",
+ cgmlp_linear_units: int = 2048,
+ cgmlp_conv_kernel: int = 31,
+ use_linear_after_conv: bool = False,
+ gate_activation: str = "identity",
+ num_blocks: int = 12,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: Optional[str] = "conv2d",
+ zero_triu: bool = False,
+ padding_idx: int = -1,
+ layer_drop_rate: float = 0.0,
+ max_pos_emb_len: int = 5000,
+ use_ffn: bool = False,
+ macaron_ffn: bool = False,
+ ffn_activation_type: str = "swish",
+ linear_units: int = 2048,
+ positionwise_layer_type: str = "linear",
+ merge_conv_kernel: int = 3,
+ interctc_layer_idx=None,
+ interctc_use_conditioning: bool = False,
+ ):
+ super().__init__()
+ self._output_size = output_size
+
+ if rel_pos_type == "legacy":
+ if pos_enc_layer_type == "rel_pos":
+ pos_enc_layer_type = "legacy_rel_pos"
+ if attention_layer_type == "rel_selfattn":
+ attention_layer_type = "legacy_rel_selfattn"
+ elif rel_pos_type == "latest":
+ assert attention_layer_type != "legacy_rel_selfattn"
+ assert pos_enc_layer_type != "legacy_rel_pos"
+ else:
+ raise ValueError("unknown rel_pos_type: " + rel_pos_type)
+
+ if pos_enc_layer_type == "abs_pos":
+ pos_enc_class = PositionalEncoding
+ elif pos_enc_layer_type == "scaled_abs_pos":
+ pos_enc_class = ScaledPositionalEncoding
+ elif pos_enc_layer_type == "rel_pos":
+ assert attention_layer_type == "rel_selfattn"
+ pos_enc_class = RelPositionalEncoding
+ elif pos_enc_layer_type == "legacy_rel_pos":
+ assert attention_layer_type == "legacy_rel_selfattn"
+ pos_enc_class = LegacyRelPositionalEncoding
+ logging.warning(
+ "Using legacy_rel_pos and it will be deprecated in the future."
+ )
+ else:
+ raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
+
+ 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),
+ pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+ )
+ elif input_layer == "conv2d":
+ self.embed = Conv2dSubsampling(
+ input_size,
+ output_size,
+ dropout_rate,
+ pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+ )
+ elif input_layer == "conv2d2":
+ self.embed = Conv2dSubsampling2(
+ input_size,
+ output_size,
+ dropout_rate,
+ pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+ )
+ elif input_layer == "conv2d6":
+ self.embed = Conv2dSubsampling6(
+ input_size,
+ output_size,
+ dropout_rate,
+ pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+ )
+ elif input_layer == "conv2d8":
+ self.embed = Conv2dSubsampling8(
+ input_size,
+ output_size,
+ dropout_rate,
+ pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+ )
+ elif input_layer == "embed":
+ self.embed = torch.nn.Sequential(
+ torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
+ pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+ )
+ elif isinstance(input_layer, torch.nn.Module):
+ self.embed = torch.nn.Sequential(
+ input_layer,
+ pos_enc_class(output_size, positional_dropout_rate, max_pos_emb_len),
+ )
+ elif input_layer is None:
+ if input_size == output_size:
+ self.embed = None
+ else:
+ self.embed = torch.nn.Linear(input_size, output_size)
+ else:
+ raise ValueError("unknown input_layer: " + input_layer)
+
+ activation = get_activation(ffn_activation_type)
+ if positionwise_layer_type == "linear":
+ positionwise_layer = PositionwiseFeedForward
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ dropout_rate,
+ activation,
+ )
+ elif positionwise_layer_type is None:
+ logging.warning("no macaron ffn")
+ else:
+ raise ValueError("Support only linear.")
+
+ if attention_layer_type == "selfattn":
+ encoder_selfattn_layer = MultiHeadedAttention
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ )
+ elif attention_layer_type == "legacy_rel_selfattn":
+ assert pos_enc_layer_type == "legacy_rel_pos"
+ encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ )
+ logging.warning(
+ "Using legacy_rel_selfattn and it will be deprecated in the future."
+ )
+ elif attention_layer_type == "rel_selfattn":
+ assert pos_enc_layer_type == "rel_pos"
+ encoder_selfattn_layer = RelPositionMultiHeadedAttention
+ encoder_selfattn_layer_args = (
+ attention_heads,
+ output_size,
+ attention_dropout_rate,
+ zero_triu,
+ )
+ elif attention_layer_type == "fast_selfattn":
+ assert pos_enc_layer_type in ["abs_pos", "scaled_abs_pos"]
+ encoder_selfattn_layer = FastSelfAttention
+ encoder_selfattn_layer_args = (
+ output_size,
+ attention_heads,
+ attention_dropout_rate,
+ )
+ else:
+ raise ValueError("unknown encoder_attn_layer: " + attention_layer_type)
+
+ cgmlp_layer = ConvolutionalGatingMLP
+ cgmlp_layer_args = (
+ output_size,
+ cgmlp_linear_units,
+ cgmlp_conv_kernel,
+ dropout_rate,
+ use_linear_after_conv,
+ gate_activation,
+ )
+
+ self.encoders = repeat(
+ num_blocks,
+ lambda lnum: EBranchformerEncoderLayer(
+ output_size,
+ encoder_selfattn_layer(*encoder_selfattn_layer_args),
+ cgmlp_layer(*cgmlp_layer_args),
+ positionwise_layer(*positionwise_layer_args) if use_ffn else None,
+ positionwise_layer(*positionwise_layer_args)
+ if use_ffn and macaron_ffn
+ else None,
+ dropout_rate,
+ merge_conv_kernel,
+ ),
+ layer_drop_rate,
+ )
+ self.after_norm = LayerNorm(output_size)
+
+ if interctc_layer_idx is None:
+ interctc_layer_idx = []
+ 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
+
+ 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,
+ max_layer: int = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+ """Calculate forward propagation.
+
+ Args:
+ xs_pad (torch.Tensor): Input tensor (#batch, L, input_size).
+ ilens (torch.Tensor): Input length (#batch).
+ prev_states (torch.Tensor): Not to be used now.
+ ctc (CTC): Intermediate CTC module.
+ max_layer (int): Layer depth below which InterCTC is applied.
+ Returns:
+ torch.Tensor: Output tensor (#batch, L, output_size).
+ torch.Tensor: Output length (#batch).
+ torch.Tensor: Not to be used now.
+ """
+
+ masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
+
+ if (
+ 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)
+ elif self.embed is not None:
+ xs_pad = self.embed(xs_pad)
+
+ intermediate_outs = []
+ if len(self.interctc_layer_idx) == 0:
+ if max_layer is not None and 0 <= max_layer < len(self.encoders):
+ for layer_idx, encoder_layer in enumerate(self.encoders):
+ xs_pad, masks = encoder_layer(xs_pad, masks)
+ if layer_idx >= max_layer:
+ break
+ else:
+ xs_pad, masks = self.encoders(xs_pad, masks)
+ else:
+ for layer_idx, encoder_layer in enumerate(self.encoders):
+ xs_pad, masks = encoder_layer(xs_pad, masks)
+
+ if layer_idx + 1 in self.interctc_layer_idx:
+ encoder_out = xs_pad
+
+ if isinstance(encoder_out, tuple):
+ encoder_out = encoder_out[0]
+
+ intermediate_outs.append((layer_idx + 1, encoder_out))
+
+ if self.interctc_use_conditioning:
+ ctc_out = ctc.softmax(encoder_out)
+
+ if isinstance(xs_pad, tuple):
+ xs_pad = list(xs_pad)
+ xs_pad[0] = xs_pad[0] + self.conditioning_layer(ctc_out)
+ xs_pad = tuple(xs_pad)
+ else:
+ xs_pad = xs_pad + self.conditioning_layer(ctc_out)
+
+ if isinstance(xs_pad, tuple):
+ xs_pad = xs_pad[0]
+
+ xs_pad = self.after_norm(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
--
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