# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Modified from wenet(https://github.com/wenet-e2e/wenet)
"""Encoder definition."""
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
import paddle
from paddle import nn
from typeguard import check_argument_types
from paddlespeech.s2t.modules.activation import get_activation
from paddlespeech.s2t.modules.align import LayerNorm
from paddlespeech.s2t.modules.align import Linear
from paddlespeech.s2t.modules.attention import MultiHeadedAttention
from paddlespeech.s2t.modules.attention import RelPositionMultiHeadedAttention
from paddlespeech.s2t.modules.attention import RoPERelPositionMultiHeadedAttention
from paddlespeech.s2t.modules.conformer_convolution import ConvolutionModule
from paddlespeech.s2t.modules.embedding import NoPositionalEncoding
from paddlespeech.s2t.modules.embedding import PositionalEncoding
from paddlespeech.s2t.modules.embedding import RelPositionalEncoding
from paddlespeech.s2t.modules.encoder_layer import ConformerEncoderLayer
from paddlespeech.s2t.modules.encoder_layer import SqueezeformerEncoderLayer
from paddlespeech.s2t.modules.encoder_layer import TransformerEncoderLayer
from paddlespeech.s2t.modules.mask import add_optional_chunk_mask
from paddlespeech.s2t.modules.mask import make_non_pad_mask
from paddlespeech.s2t.modules.positionwise_feed_forward import PositionwiseFeedForward
from paddlespeech.s2t.modules.subsampling import Conv2dSubsampling4
from paddlespeech.s2t.modules.subsampling import Conv2dSubsampling6
from paddlespeech.s2t.modules.subsampling import Conv2dSubsampling8
from paddlespeech.s2t.modules.subsampling import DepthwiseConv2DSubsampling4
from paddlespeech.s2t.modules.subsampling import LinearNoSubsampling
from paddlespeech.s2t.modules.time_reduction import TimeReductionLayer1D
from paddlespeech.s2t.modules.time_reduction import TimeReductionLayer2D
from paddlespeech.s2t.modules.time_reduction import TimeReductionLayerStream
from paddlespeech.s2t.utils.log import Log
logger = Log(__name__).getlog()
__all__ = [
"BaseEncoder", 'TransformerEncoder', "ConformerEncoder",
"SqueezeformerEncoder"
]
class BaseEncoder(nn.Layer):
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: str="conv2d",
pos_enc_layer_type: str="abs_pos",
normalize_before: bool=True,
concat_after: bool=False,
static_chunk_size: int=0,
use_dynamic_chunk: bool=False,
global_cmvn: paddle.nn.Layer=None,
use_dynamic_left_chunk: bool=False,
max_len: int=5000):
"""
Args:
input_size (int): input dim, d_feature
output_size (int): dimension of attention, d_model
attention_heads (int): the number of heads of multi head attention
linear_units (int): the hidden units number of position-wise feed
forward
num_blocks (int): the number of encoder blocks
dropout_rate (float): dropout rate
attention_dropout_rate (float): dropout rate in attention
positional_dropout_rate (float): dropout rate after adding
positional encoding
input_layer (str): input layer type.
optional [linear, conv2d, conv2d6, conv2d8]
pos_enc_layer_type (str): Encoder positional encoding layer type.
opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
normalize_before (bool):
True: use layer_norm before each sub-block of a layer.
False: use layer_norm after each sub-block of a layer.
concat_after (bool): whether to concat attention layer's input
and output.
True: x -> x + linear(concat(x, att(x)))
False: x -> x + att(x)
static_chunk_size (int): chunk size for static chunk training and
decoding
use_dynamic_chunk (bool): whether use dynamic chunk size for
training or not, You can only use fixed chunk(chunk_size > 0)
or dyanmic chunk size(use_dynamic_chunk = True)
global_cmvn (Optional[paddle.nn.Layer]): Optional GlobalCMVN layer
use_dynamic_left_chunk (bool): whether use dynamic left chunk in
dynamic chunk training
"""
assert check_argument_types()
super().__init__()
self._output_size = output_size
if pos_enc_layer_type == "abs_pos":
pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "rel_pos":
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "rope_pos":
pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "no_pos":
pos_enc_class = NoPositionalEncoding
else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
if input_layer == "linear":
subsampling_class = LinearNoSubsampling
elif input_layer == "conv2d":
subsampling_class = Conv2dSubsampling4
elif input_layer == "conv2d6":
subsampling_class = Conv2dSubsampling6
elif input_layer == "conv2d8":
subsampling_class = Conv2dSubsampling8
else:
raise ValueError("unknown input_layer: " + input_layer)
self.global_cmvn = global_cmvn
self.embed = subsampling_class(
idim=input_size,
odim=output_size,
dropout_rate=dropout_rate,
pos_enc_class=pos_enc_class(
d_model=output_size,
dropout_rate=positional_dropout_rate,
max_len=max_len), )
self.normalize_before = normalize_before
self.after_norm = LayerNorm(output_size, epsilon=1e-12)
self.static_chunk_size = static_chunk_size
self.use_dynamic_chunk = use_dynamic_chunk
self.use_dynamic_left_chunk = use_dynamic_left_chunk
def output_size(self) -> int:
return self._output_size
def forward(
self,
xs: paddle.Tensor,
xs_lens: paddle.Tensor,
decoding_chunk_size: int=0,
num_decoding_left_chunks: int=-1,
) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Embed positions in tensor.
Args:
xs: padded input tensor (B, L, D)
xs_lens: input length (B)
decoding_chunk_size: decoding chunk size for dynamic chunk
0: default for training, use random dynamic chunk.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
num_decoding_left_chunks: number of left chunks, this is for decoding,
the chunk size is decoding_chunk_size.
>=0: use num_decoding_left_chunks
<0: use all left chunks
Returns:
encoder output tensor, lens and mask
"""
masks = make_non_pad_mask(xs_lens).unsqueeze(1) # (B, 1, L)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
xs, pos_emb, masks = self.embed(xs, masks, offset=0)
mask_pad = ~masks
chunk_masks = add_optional_chunk_mask(
xs, masks, self.use_dynamic_chunk, self.use_dynamic_left_chunk,
decoding_chunk_size, self.static_chunk_size,
num_decoding_left_chunks)
for layer in self.encoders:
xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
if self.normalize_before:
xs = self.after_norm(xs)
# Here we assume the mask is not changed in encoder layers, so just
# return the masks before encoder layers, and the masks will be used
# for cross attention with decoder later
return xs, masks
def forward_chunk(
self,
xs: paddle.Tensor,
offset: int,
required_cache_size: int,
att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
att_mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool)
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
""" Forward just one chunk
Args:
xs (paddle.Tensor): chunk audio feat input, [B=1, T, D], where
`T==(chunk_size-1)*subsampling_rate + subsample.right_context + 1`
offset (int): current offset in encoder output time stamp
required_cache_size (int): cache size required for next chunk
compuation
>=0: actual cache size
<0: means all history cache is required
att_cache(paddle.Tensor): cache tensor for key & val in
transformer/conformer attention. Shape is
(elayers, head, cache_t1, d_k * 2), where`head * d_k == hidden-dim`
and `cache_t1 == chunk_size * num_decoding_left_chunks`.
cnn_cache (paddle.Tensor): cache tensor for cnn_module in conformer,
(elayers, B=1, hidden-dim, cache_t2), where `cache_t2 == cnn.lorder - 1`
Returns:
paddle.Tensor: output of current input xs, (B=1, chunk_size, hidden-dim)
paddle.Tensor: new attention cache required for next chunk, dyanmic shape
(elayers, head, T, d_k*2) depending on required_cache_size
paddle.Tensor: new conformer cnn cache required for next chunk, with
same shape as the original cnn_cache
"""
assert xs.shape[0] == 1 # batch size must be one
# tmp_masks is just for interface compatibility, [B=1, C=1, T]
tmp_masks = paddle.ones([1, 1, xs.shape[1]], dtype=paddle.bool)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
# before embed, xs=(B, T, D1), pos_emb=(B=1, T, D)
xs, _, _ = self.embed(xs, tmp_masks, offset=offset)
# after embed, xs=(B=1, chunk_size, hidden-dim)
elayers, _, cache_t1, _ = att_cache.shape
chunk_size = xs.shape[1]
attention_key_size = cache_t1 + chunk_size
# only used when using `RelPositionMultiHeadedAttention` and `RoPERelPositionMultiHeadedAttention`
pos_emb = self.embed.position_encoding(
offset=offset - cache_t1, size=attention_key_size)
if required_cache_size < 0:
next_cache_start = 0
elif required_cache_size == 0:
next_cache_start = attention_key_size
else:
next_cache_start = max(attention_key_size - required_cache_size, 0)
r_att_cache = []
r_cnn_cache = []
for i, layer in enumerate(self.encoders):
# att_cache[i:i+1] = (1, head, cache_t1, d_k*2)
# cnn_cache[i:i+1] = (1, B=1, hidden-dim, cache_t2)
# WARNING: eliminate if-else cond op in graph
# tensor zeros([0,0,0,0]) support [i:i+1] slice, will return zeros([0,0,0,0]) tensor
# raw code as below:
# att_cache=att_cache[i:i+1] if elayers > 0 else att_cache,
# cnn_cache=cnn_cache[i:i+1] if cnn_cache.shape[0] > 0 else cnn_cache,
xs, _, new_att_cache, new_cnn_cache = layer(
xs,
att_mask,
pos_emb,
att_cache=att_cache[i:i + 1],
cnn_cache=cnn_cache[i:i + 1], )
# new_att_cache = (1, head, attention_key_size, d_k*2)
# new_cnn_cache = (B=1, hidden-dim, cache_t2)
r_att_cache.append(new_att_cache[:, :, next_cache_start:, :])
r_cnn_cache.append(new_cnn_cache) # add elayer dim
if self.normalize_before:
xs = self.after_norm(xs)
# r_att_cache (elayers, head, T, d_k*2)
# r_cnn_cache (elayers, B=1, hidden-dim, cache_t2)
r_att_cache = paddle.concat(r_att_cache, axis=0)
r_cnn_cache = paddle.stack(r_cnn_cache, axis=0)
return xs, r_att_cache, r_cnn_cache
def forward_chunk_by_chunk(
self,
xs: paddle.Tensor,
decoding_chunk_size: int,
num_decoding_left_chunks: int=-1,
) -> Tuple[paddle.Tensor, paddle.Tensor]:
""" Forward input chunk by chunk with chunk_size like a streaming
fashion
Here we should pay special attention to computation cache in the
streaming style forward chunk by chunk. Three things should be taken
into account for computation in the current network:
1. transformer/conformer encoder layers output cache
2. convolution in conformer
3. convolution in subsampling
However, we don't implement subsampling cache for:
1. We can control subsampling module to output the right result by
overlapping input instead of cache left context, even though it
wastes some computation, but subsampling only takes a very
small fraction of computation in the whole model.
2. Typically, there are several covolution layers with subsampling
in subsampling module, it is tricky and complicated to do cache
with different convolution layers with different subsampling
rate.
3. Currently, nn.Sequential is used to stack all the convolution
layers in subsampling, we need to rewrite it to make it work
with cache, which is not prefered.
Args:
xs (paddle.Tensor): (1, max_len, dim)
chunk_size (int): decoding chunk size.
num_left_chunks (int): decoding with num left chunks.
"""
assert decoding_chunk_size > 0
# The model is trained by static or dynamic chunk
assert self.static_chunk_size > 0 or self.use_dynamic_chunk
# feature stride and window for `subsampling` module
subsampling = self.embed.subsampling_rate
context = self.embed.right_context + 1 # Add current frame
stride = subsampling * decoding_chunk_size
decoding_window = (decoding_chunk_size - 1) * subsampling + context
num_frames = xs.shape[1]
required_cache_size = decoding_chunk_size * num_decoding_left_chunks
att_cache: paddle.Tensor = paddle.zeros([0, 0, 0, 0])
cnn_cache: paddle.Tensor = paddle.zeros([0, 0, 0, 0])
outputs = []
offset = 0
# Feed forward overlap input step by step
for cur in range(0, num_frames - context + 1, stride):
end = min(cur + decoding_window, num_frames)
chunk_xs = xs[:, cur:end, :]
(y, att_cache, cnn_cache) = self.forward_chunk(
chunk_xs, offset, required_cache_size, att_cache, cnn_cache)
outputs.append(y)
offset += y.shape[1]
ys = paddle.cat(outputs, 1)
masks = paddle.ones([1, 1, ys.shape[1]], dtype=paddle.bool)
return ys, masks
class TransformerEncoder(BaseEncoder):
"""Transformer encoder module."""
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: str="conv2d",
pos_enc_layer_type: str="abs_pos",
normalize_before: bool=True,
concat_after: bool=False,
static_chunk_size: int=0,
use_dynamic_chunk: bool=False,
global_cmvn: nn.Layer=None,
use_dynamic_left_chunk: bool=False, ):
""" Construct TransformerEncoder
See Encoder for the meaning of each parameter.
"""
assert check_argument_types()
super().__init__(input_size, output_size, attention_heads, linear_units,
num_blocks, dropout_rate, positional_dropout_rate,
attention_dropout_rate, input_layer,
pos_enc_layer_type, normalize_before, concat_after,
static_chunk_size, use_dynamic_chunk, global_cmvn,
use_dynamic_left_chunk)
self.encoders = nn.LayerList([
TransformerEncoderLayer(
size=output_size,
self_attn=MultiHeadedAttention(attention_heads, output_size,
attention_dropout_rate),
feed_forward=PositionwiseFeedForward(output_size, linear_units,
dropout_rate),
dropout_rate=dropout_rate,
normalize_before=normalize_before,
concat_after=concat_after) for _ in range(num_blocks)
])
def forward_one_step(
self,
xs: paddle.Tensor,
masks: paddle.Tensor,
cache=None, ) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Encode input frame.
Args:
xs (paddle.Tensor): (Prefix) Input tensor. (B, T, D)
masks (paddle.Tensor): Mask tensor. (B, T, T)
cache (List[paddle.Tensor]): List of cache tensors.
Returns:
paddle.Tensor: Output tensor.
paddle.Tensor: Mask tensor.
List[paddle.Tensor]: List of new cache tensors.
"""
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
xs, pos_emb, masks = self.embed(xs, masks, offset=0)
if cache is None:
cache = [None for _ in range(len(self.encoders))]
new_cache = []
for c, e in zip(cache, self.encoders):
xs, masks, _ = e(xs, masks, output_cache=c)
new_cache.append(xs)
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks, new_cache
class ConformerEncoder(BaseEncoder):
"""Conformer encoder module."""
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: str="conv2d",
pos_enc_layer_type: str="rel_pos",
normalize_before: bool=True,
concat_after: bool=False,
static_chunk_size: int=0,
use_dynamic_chunk: bool=False,
global_cmvn: nn.Layer=None,
use_dynamic_left_chunk: bool=False,
positionwise_conv_kernel_size: int=1,
macaron_style: bool=True,
selfattention_layer_type: str="rel_selfattn",
activation_type: str="swish",
use_cnn_module: bool=True,
cnn_module_kernel: int=15,
causal: bool=False,
cnn_module_norm: str="batch_norm",
max_len: int=5000):
"""Construct ConformerEncoder
Args:
input_size to use_dynamic_chunk, see in BaseEncoder
positionwise_conv_kernel_size (int): Kernel size of positionwise
conv1d layer.
macaron_style (bool): Whether to use macaron style for
positionwise layer.
selfattention_layer_type (str): Encoder attention layer type,
the parameter has no effect now, it's just for configure
compatibility.
activation_type (str): Encoder activation function type.
use_cnn_module (bool): Whether to use convolution module.
cnn_module_kernel (int): Kernel size of convolution module.
causal (bool): whether to use causal convolution or not.
cnn_module_norm (str): cnn conv norm type, Optional['batch_norm','layer_norm']
"""
assert check_argument_types()
super().__init__(input_size, output_size, attention_heads, linear_units,
num_blocks, dropout_rate, positional_dropout_rate,
attention_dropout_rate, input_layer,
pos_enc_layer_type, normalize_before, concat_after,
static_chunk_size, use_dynamic_chunk, global_cmvn,
use_dynamic_left_chunk, max_len)
activation = get_activation(activation_type)
# self-attention module definition
encoder_dim = output_size
if pos_enc_layer_type == "abs_pos":
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, encoder_dim,
attention_dropout_rate)
elif pos_enc_layer_type == "rel_pos":
encoder_selfattn_layer = RelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, encoder_dim,
attention_dropout_rate)
elif pos_enc_layer_type == "rope_pos":
encoder_selfattn_layer = RoPERelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, encoder_dim,
attention_dropout_rate)
else:
raise ValueError(
f"pos_enc_layer_type {pos_enc_layer_type} not supported.")
# feed-forward module definition
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (encoder_dim, linear_units, dropout_rate,
activation)
# convolution module definition
convolution_layer = ConvolutionModule
convolution_layer_args = (encoder_dim, cnn_module_kernel, activation,
cnn_module_norm, causal)
self.encoders = nn.LayerList([
ConformerEncoderLayer(
size=encoder_dim,
self_attn=encoder_selfattn_layer(*encoder_selfattn_layer_args),
feed_forward=positionwise_layer(*positionwise_layer_args),
feed_forward_macaron=positionwise_layer(
*positionwise_layer_args) if macaron_style else None,
conv_module=convolution_layer(*convolution_layer_args)
if use_cnn_module else None,
dropout_rate=dropout_rate,
normalize_before=normalize_before,
concat_after=concat_after) for _ in range(num_blocks)
])
class SqueezeformerEncoder(nn.Layer):
def __init__(self,
input_size: int,
encoder_dim: int=256,
output_size: int=256,
attention_heads: int=4,
num_blocks: int=12,
reduce_idx: Optional[Union[int, List[int]]]=5,
recover_idx: Optional[Union[int, List[int]]]=11,
feed_forward_expansion_factor: int=4,
dw_stride: bool=False,
input_dropout_rate: float=0.1,
pos_enc_layer_type: str="rel_pos",
time_reduction_layer_type: str="conv1d",
feed_forward_dropout_rate: float=0.1,
attention_dropout_rate: float=0.1,
cnn_module_kernel: int=31,
cnn_norm_type: str="layer_norm",
dropout: float=0.1,
causal: bool=False,
adaptive_scale: bool=True,
activation_type: str="swish",
init_weights: bool=True,
global_cmvn: paddle.nn.Layer=None,
normalize_before: bool=False,
use_dynamic_chunk: bool=False,
concat_after: bool=False,
static_chunk_size: int=0,
use_dynamic_left_chunk: bool=False):
"""Construct SqueezeformerEncoder
Args:
input_size to use_dynamic_chunk, see in Transformer BaseEncoder.
encoder_dim (int): The hidden dimension of encoder layer.
output_size (int): The output dimension of final projection layer.
attention_heads (int): Num of attention head in attention module.
num_blocks (int): Num of encoder layers.
reduce_idx Optional[Union[int, List[int]]]:
reduce layer index, from 40ms to 80ms per frame.
recover_idx Optional[Union[int, List[int]]]:
recover layer index, from 80ms to 40ms per frame.
feed_forward_expansion_factor (int): Enlarge coefficient of FFN.
dw_stride (bool): Whether do depthwise convolution
on subsampling module.
input_dropout_rate (float): Dropout rate of input projection layer.
pos_enc_layer_type (str): Self attention type.
time_reduction_layer_type (str): Conv1d or Conv2d reduction layer.
cnn_module_kernel (int): Kernel size of CNN module.
activation_type (str): Encoder activation function type.
cnn_module_kernel (int): Kernel size of convolution module.
adaptive_scale (bool): Whether to use adaptive scale.
init_weights (bool): Whether to initialize weights.
causal (bool): whether to use causal convolution or not.
"""
assert check_argument_types()
super().__init__()
self.global_cmvn = global_cmvn
self.reduce_idx: Optional[Union[int, List[int]]] = [reduce_idx] \
if type(reduce_idx) == int else reduce_idx
self.recover_idx: Optional[Union[int, List[int]]] = [recover_idx] \
if type(recover_idx) == int else recover_idx
self.check_ascending_list()
if reduce_idx is None:
self.time_reduce = None
else:
if recover_idx is None:
self.time_reduce = 'normal' # no recovery at the end
else:
self.time_reduce = 'recover' # recovery at the end
assert len(self.reduce_idx) == len(self.recover_idx)
self.reduce_stride = 2
self._output_size = output_size
self.normalize_before = normalize_before
self.static_chunk_size = static_chunk_size
self.use_dynamic_chunk = use_dynamic_chunk
self.use_dynamic_left_chunk = use_dynamic_left_chunk
activation = get_activation(activation_type)
# self-attention module definition
if pos_enc_layer_type == "abs_pos":
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, output_size,
attention_dropout_rate)
elif pos_enc_layer_type == "rel_pos":
encoder_selfattn_layer = RelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, encoder_dim,
attention_dropout_rate,
adaptive_scale, init_weights)
elif pos_enc_layer_type == "rope_pos":
encoder_selfattn_layer = RoPERelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (attention_heads, encoder_dim,
attention_dropout_rate,
adaptive_scale, init_weights)
else:
raise ValueError(
f"pos_enc_layer_type {pos_enc_layer_type} not supported.")
# feed-forward module definition
positionwise_layer = PositionwiseFeedForward
positionwise_layer_args = (
encoder_dim, encoder_dim * feed_forward_expansion_factor,
feed_forward_dropout_rate, activation, adaptive_scale, init_weights)
# convolution module definition
convolution_layer = ConvolutionModule
convolution_layer_args = (encoder_dim, cnn_module_kernel, activation,
cnn_norm_type, causal, True, adaptive_scale,
init_weights)
self.embed = DepthwiseConv2DSubsampling4(
1, encoder_dim,
RelPositionalEncoding(encoder_dim, dropout_rate=0.1), dw_stride,
input_size, input_dropout_rate, init_weights)
self.preln = LayerNorm(encoder_dim)
self.encoders = paddle.nn.LayerList([
SqueezeformerEncoderLayer(
encoder_dim,
encoder_selfattn_layer(*encoder_selfattn_layer_args),
positionwise_layer(*positionwise_layer_args),
convolution_layer(*convolution_layer_args),
positionwise_layer(*positionwise_layer_args), normalize_before,
dropout, concat_after) for _ in range(num_blocks)
])
if time_reduction_layer_type == 'conv1d':
time_reduction_layer = TimeReductionLayer1D
time_reduction_layer_args = {
'channel': encoder_dim,
'out_dim': encoder_dim,
}
elif time_reduction_layer_type == 'stream':
time_reduction_layer = TimeReductionLayerStream
time_reduction_layer_args = {
'channel': encoder_dim,
'out_dim': encoder_dim,
}
else:
time_reduction_layer = TimeReductionLayer2D
time_reduction_layer_args = {'encoder_dim': encoder_dim}
self.time_reduction_layer = time_reduction_layer(
**time_reduction_layer_args)
self.time_recover_layer = Linear(encoder_dim, encoder_dim)
self.final_proj = None
if output_size != encoder_dim:
self.final_proj = Linear(encoder_dim, output_size)
def output_size(self) -> int:
return self._output_size
def forward(
self,
xs: paddle.Tensor,
xs_lens: paddle.Tensor,
decoding_chunk_size: int=0,
num_decoding_left_chunks: int=-1,
) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Embed positions in tensor.
Args:
xs: padded input tensor (B, L, D)
xs_lens: input length (B)
decoding_chunk_size: decoding chunk size for dynamic chunk
0: default for training, use random dynamic chunk.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
num_decoding_left_chunks: number of left chunks, this is for decoding,
the chunk size is decoding_chunk_size.
>=0: use num_decoding_left_chunks
<0: use all left chunks
Returns:
encoder output tensor, lens and mask
"""
masks = make_non_pad_mask(xs_lens).unsqueeze(1) # (B, 1, L)
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
xs, pos_emb, masks = self.embed(xs, masks)
mask_pad = masks
chunk_masks = add_optional_chunk_mask(
xs, masks, self.use_dynamic_chunk, self.use_dynamic_left_chunk,
decoding_chunk_size, self.static_chunk_size,
num_decoding_left_chunks)
xs_lens = chunk_masks.squeeze(1).sum(1)
xs = self.preln(xs)
recover_activations: \
List[Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]] = []
index = 0
for i, layer in enumerate(self.encoders):
if self.reduce_idx is not None:
if self.time_reduce is not None and i in self.reduce_idx:
recover_activations.append(
(xs, chunk_masks, pos_emb, mask_pad))
xs, xs_lens, chunk_masks, mask_pad = self.time_reduction_layer(
xs, xs_lens, chunk_masks, mask_pad)
pos_emb = pos_emb[:, ::2, :]
index += 1
if self.recover_idx is not None:
if self.time_reduce == 'recover' and i in self.recover_idx:
index -= 1
recover_tensor, recover_chunk_masks, recover_pos_emb, recover_mask_pad = recover_activations[
index]
# recover output length for ctc decode
xs = paddle.repeat_interleave(xs, repeats=2, axis=1)
xs = self.time_recover_layer(xs)
recoverd_t = recover_tensor.shape[1]
xs = recover_tensor + xs[:, :recoverd_t, :]
chunk_masks = recover_chunk_masks
pos_emb = recover_pos_emb
mask_pad = recover_mask_pad
xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
if self.final_proj is not None:
xs = self.final_proj(xs)
return xs, masks
def check_ascending_list(self):
if self.reduce_idx is not None:
assert self.reduce_idx == sorted(self.reduce_idx), \
"reduce_idx should be int or ascending list"
if self.recover_idx is not None:
assert self.recover_idx == sorted(self.recover_idx), \
"recover_idx should be int or ascending list"
def calculate_downsampling_factor(self, i: int) -> int:
if self.reduce_idx is None:
return 1
else:
reduce_exp, recover_exp = 0, 0
for exp, rd_idx in enumerate(self.reduce_idx):
if i >= rd_idx:
reduce_exp = exp + 1
if self.recover_idx is not None:
for exp, rc_idx in enumerate(self.recover_idx):
if i >= rc_idx:
recover_exp = exp + 1
return int(2**(reduce_exp - recover_exp))
def forward_chunk(
self,
xs: paddle.Tensor,
offset: int,
required_cache_size: int,
att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
att_mask: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
""" Forward just one chunk
Args:
xs (paddle.Tensor): chunk input, with shape (b=1, time, mel-dim),
where `time == (chunk_size - 1) * subsample_rate + \
subsample.right_context + 1`
offset (int): current offset in encoder output time stamp
required_cache_size (int): cache size required for next chunk
compuation
>=0: actual cache size
<0: means all history cache is required
att_cache (paddle.Tensor): cache tensor for KEY & VALUE in
transformer/conformer attention, with shape
(elayers, head, cache_t1, d_k * 2), where
`head * d_k == hidden-dim` and
`cache_t1 == chunk_size * num_decoding_left_chunks`.
cnn_cache (paddle.Tensor): cache tensor for cnn_module in conformer,
(elayers, b=1, hidden-dim, cache_t2), where
`cache_t2 == cnn.lorder - 1`
Returns:
paddle.Tensor: output of current input xs,
with shape (b=1, chunk_size, hidden-dim).
paddle.Tensor: new attention cache required for next chunk, with
dynamic shape (elayers, head, ?, d_k * 2)
depending on required_cache_size.
paddle.Tensor: new conformer cnn cache required for next chunk, with
same shape as the original cnn_cache.
"""
assert xs.shape[0] == 1 # batch size must be one
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
# tmp_masks is just for interface compatibility, [B=1, C=1, T]
tmp_masks = paddle.ones([1, 1, xs.shape[1]], dtype=paddle.bool)
# before embed, xs=(B, T, D1), pos_emb=(B=1, T, D)
xs, pos_emb, _ = self.embed(xs, tmp_masks, offset=offset)
# NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim)
elayers, cache_t1 = att_cache.shape[0], att_cache.shape[2]
chunk_size = xs.shape[1]
attention_key_size = cache_t1 + chunk_size
pos_emb = self.embed.position_encoding(
offset=offset - cache_t1, size=attention_key_size)
if required_cache_size < 0:
next_cache_start = 0
elif required_cache_size == 0:
next_cache_start = attention_key_size
else:
next_cache_start = max(attention_key_size - required_cache_size, 0)
r_att_cache = []
r_cnn_cache = []
mask_pad = paddle.ones([1, xs.shape[1]], dtype=paddle.bool)
mask_pad = mask_pad.unsqueeze(1)
max_att_len: int = 0
recover_activations: \
List[Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]] = []
index = 0
xs_lens = paddle.to_tensor([xs.shape[1]], dtype=paddle.int32)
xs = self.preln(xs)
for i, layer in enumerate(self.encoders):
# NOTE(xcsong): Before layer.forward
# shape(att_cache[i:i + 1]) is (1, head, cache_t1, d_k * 2),
# shape(cnn_cache[i]) is (b=1, hidden-dim, cache_t2)
if self.reduce_idx is not None:
if self.time_reduce is not None and i in self.reduce_idx:
recover_activations.append(
(xs, att_mask, pos_emb, mask_pad))
xs, xs_lens, att_mask, mask_pad = self.time_reduction_layer(
xs, xs_lens, att_mask, mask_pad)
pos_emb = pos_emb[:, ::2, :]
index += 1
if self.recover_idx is not None:
if self.time_reduce == 'recover' and i in self.recover_idx:
index -= 1
recover_tensor, recover_att_mask, recover_pos_emb, recover_mask_pad = recover_activations[
index]
# recover output length for ctc decode
xs = paddle.repeat_interleave(xs, repeats=2, axis=1)
xs = self.time_recover_layer(xs)
recoverd_t = recover_tensor.shape[1]
xs = recover_tensor + xs[:, :recoverd_t, :]
att_mask = recover_att_mask
pos_emb = recover_pos_emb
mask_pad = recover_mask_pad
factor = self.calculate_downsampling_factor(i)
att_cache1 = att_cache[
i:i + 1][:, :, ::factor, :][:, :, :pos_emb.shape[1] - xs.shape[
1], :]
cnn_cache1 = cnn_cache[i] if cnn_cache.shape[0] > 0 else cnn_cache
xs, _, new_att_cache, new_cnn_cache = layer(
xs,
att_mask,
pos_emb,
att_cache=att_cache1,
cnn_cache=cnn_cache1)
# NOTE(xcsong): After layer.forward
# shape(new_att_cache) is (1, head, attention_key_size, d_k * 2),
# shape(new_cnn_cache) is (b=1, hidden-dim, cache_t2)
cached_att = new_att_cache[:, :, next_cache_start // factor:, :]
cached_cnn = new_cnn_cache.unsqueeze(0)
cached_att = cached_att.repeat_interleave(repeats=factor, axis=2)
if i == 0:
# record length for the first block as max length
max_att_len = cached_att.shape[2]
r_att_cache.append(cached_att[:, :, :max_att_len, :])
r_cnn_cache.append(cached_cnn)
# NOTE(xcsong): shape(r_att_cache) is (elayers, head, ?, d_k * 2),
# ? may be larger than cache_t1, it depends on required_cache_size
r_att_cache = paddle.concat(r_att_cache, axis=0)
# NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
r_cnn_cache = paddle.concat(r_cnn_cache, axis=0)
if self.final_proj is not None:
xs = self.final_proj(xs)
return xs, r_att_cache, r_cnn_cache