451 lines
19 KiB
Python
451 lines
19 KiB
Python
#!/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
|
|
# Copyright 2019 Mobvoi Inc. All Rights Reserved.
|
|
# Author: di.wu@mobvoi.com (DI WU)
|
|
"""Encoder definition."""
|
|
from typing import Tuple, List, Optional
|
|
|
|
import torch
|
|
from typeguard import check_argument_types
|
|
|
|
from wenet.transformer.attention import MultiHeadedAttention
|
|
from wenet.transformer.attention import RelPositionMultiHeadedAttention
|
|
from wenet.transformer.convolution import ConvolutionModule
|
|
from wenet.transformer.embedding import PositionalEncoding
|
|
from wenet.transformer.embedding import RelPositionalEncoding
|
|
from wenet.transformer.embedding import NoPositionalEncoding
|
|
from wenet.transformer.encoder_layer import TransformerEncoderLayer
|
|
from wenet.transformer.encoder_layer import ConformerEncoderLayer
|
|
from wenet.transformer.positionwise_feed_forward import PositionwiseFeedForward
|
|
from wenet.transformer.subsampling import Conv2dSubsampling4
|
|
from wenet.transformer.subsampling import Conv2dSubsampling6
|
|
from wenet.transformer.subsampling import Conv2dSubsampling8
|
|
from wenet.transformer.subsampling import LinearNoSubsampling
|
|
from wenet.utils.common import get_activation
|
|
from wenet.utils.mask import make_pad_mask
|
|
from wenet.utils.mask import add_optional_chunk_mask
|
|
|
|
|
|
class BaseEncoder(torch.nn.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: torch.nn.Module = None,
|
|
use_dynamic_left_chunk: bool = False,
|
|
):
|
|
"""
|
|
Args:
|
|
input_size (int): input dim
|
|
output_size (int): dimension of attention
|
|
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 decoder 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[torch.nn.Module]): Optional GlobalCMVN module
|
|
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 == "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(
|
|
input_size,
|
|
output_size,
|
|
dropout_rate,
|
|
pos_enc_class(output_size, positional_dropout_rate),
|
|
)
|
|
|
|
self.normalize_before = normalize_before
|
|
self.after_norm = torch.nn.LayerNorm(output_size, eps=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: torch.Tensor,
|
|
xs_lens: torch.Tensor,
|
|
decoding_chunk_size: int = 0,
|
|
num_decoding_left_chunks: int = -1,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""Embed positions in tensor.
|
|
|
|
Args:
|
|
xs: padded input tensor (B, T, 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 xs, and subsampled masks
|
|
xs: padded output tensor (B, T' ~= T/subsample_rate, D)
|
|
masks: torch.Tensor batch padding mask after subsample
|
|
(B, 1, T' ~= T/subsample_rate)
|
|
"""
|
|
masks = ~make_pad_mask(xs_lens).unsqueeze(1) # (B, 1, T)
|
|
if self.global_cmvn is not None:
|
|
xs = self.global_cmvn(xs)
|
|
xs, pos_emb, masks = self.embed(xs, masks)
|
|
mask_pad = masks # (B, 1, T/subsample_rate)
|
|
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: torch.Tensor,
|
|
offset: int,
|
|
required_cache_size: int,
|
|
subsampling_cache: Optional[torch.Tensor] = None,
|
|
elayers_output_cache: Optional[List[torch.Tensor]] = None,
|
|
conformer_cnn_cache: Optional[List[torch.Tensor]] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor],
|
|
List[torch.Tensor]]:
|
|
""" Forward just one chunk
|
|
|
|
Args:
|
|
xs (torch.Tensor): chunk input
|
|
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
|
|
subsampling_cache (Optional[torch.Tensor]): subsampling cache
|
|
elayers_output_cache (Optional[List[torch.Tensor]]):
|
|
transformer/conformer encoder layers output cache
|
|
conformer_cnn_cache (Optional[List[torch.Tensor]]): conformer
|
|
cnn cache
|
|
|
|
Returns:
|
|
torch.Tensor: output of current input xs
|
|
torch.Tensor: subsampling cache required for next chunk computation
|
|
List[torch.Tensor]: encoder layers output cache required for next
|
|
chunk computation
|
|
List[torch.Tensor]: conformer cnn cache
|
|
|
|
"""
|
|
assert xs.size(0) == 1
|
|
# tmp_masks is just for interface compatibility
|
|
tmp_masks = torch.ones(1,
|
|
xs.size(1),
|
|
device=xs.device,
|
|
dtype=torch.bool)
|
|
tmp_masks = tmp_masks.unsqueeze(1)
|
|
if self.global_cmvn is not None:
|
|
xs = self.global_cmvn(xs)
|
|
xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
|
|
if subsampling_cache is not None:
|
|
cache_size = subsampling_cache.size(1)
|
|
xs = torch.cat((subsampling_cache, xs), dim=1)
|
|
else:
|
|
cache_size = 0
|
|
pos_emb = self.embed.position_encoding(offset - cache_size, xs.size(1))
|
|
if required_cache_size < 0:
|
|
next_cache_start = 0
|
|
elif required_cache_size == 0:
|
|
next_cache_start = xs.size(1)
|
|
else:
|
|
next_cache_start = max(xs.size(1) - required_cache_size, 0)
|
|
r_subsampling_cache = xs[:, next_cache_start:, :]
|
|
# Real mask for transformer/conformer layers
|
|
masks = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
|
|
masks = masks.unsqueeze(1)
|
|
r_elayers_output_cache = []
|
|
r_conformer_cnn_cache = []
|
|
for i, layer in enumerate(self.encoders):
|
|
if elayers_output_cache is None:
|
|
attn_cache = None
|
|
else:
|
|
attn_cache = elayers_output_cache[i]
|
|
if conformer_cnn_cache is None:
|
|
cnn_cache = None
|
|
else:
|
|
cnn_cache = conformer_cnn_cache[i]
|
|
xs, _, new_cnn_cache = layer(xs,
|
|
masks,
|
|
pos_emb,
|
|
output_cache=attn_cache,
|
|
cnn_cache=cnn_cache)
|
|
r_elayers_output_cache.append(xs[:, next_cache_start:, :])
|
|
r_conformer_cnn_cache.append(new_cnn_cache)
|
|
if self.normalize_before:
|
|
xs = self.after_norm(xs)
|
|
|
|
return (xs[:, cache_size:, :], r_subsampling_cache,
|
|
r_elayers_output_cache, r_conformer_cnn_cache)
|
|
|
|
def forward_chunk_by_chunk(
|
|
self,
|
|
xs: torch.Tensor,
|
|
decoding_chunk_size: int,
|
|
num_decoding_left_chunks: int = -1,
|
|
) -> Tuple[torch.Tensor, torch.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 (torch.Tensor): (1, max_len, dim)
|
|
chunk_size (int): decoding chunk size
|
|
"""
|
|
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
|
|
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.size(1)
|
|
subsampling_cache: Optional[torch.Tensor] = None
|
|
elayers_output_cache: Optional[List[torch.Tensor]] = None
|
|
conformer_cnn_cache: Optional[List[torch.Tensor]] = None
|
|
outputs = []
|
|
offset = 0
|
|
required_cache_size = decoding_chunk_size * num_decoding_left_chunks
|
|
|
|
# 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, subsampling_cache, elayers_output_cache,
|
|
conformer_cnn_cache) = self.forward_chunk(chunk_xs, offset,
|
|
required_cache_size,
|
|
subsampling_cache,
|
|
elayers_output_cache,
|
|
conformer_cnn_cache)
|
|
outputs.append(y)
|
|
offset += y.size(1)
|
|
ys = torch.cat(outputs, 1)
|
|
masks = torch.ones(1, ys.size(1), device=ys.device, dtype=torch.bool)
|
|
masks = masks.unsqueeze(1)
|
|
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: torch.nn.Module = 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 = torch.nn.ModuleList([
|
|
TransformerEncoderLayer(
|
|
output_size,
|
|
MultiHeadedAttention(attention_heads, output_size,
|
|
attention_dropout_rate),
|
|
PositionwiseFeedForward(output_size, linear_units,
|
|
dropout_rate), dropout_rate,
|
|
normalize_before, concat_after) for _ in range(num_blocks)
|
|
])
|
|
|
|
|
|
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: torch.nn.Module = 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",
|
|
):
|
|
"""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.
|
|
"""
|
|
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)
|
|
activation = get_activation(activation_type)
|
|
|
|
# self-attention module definition
|
|
if pos_enc_layer_type == "no_pos":
|
|
encoder_selfattn_layer = MultiHeadedAttention
|
|
else:
|
|
encoder_selfattn_layer = RelPositionMultiHeadedAttention
|
|
encoder_selfattn_layer_args = (
|
|
attention_heads,
|
|
output_size,
|
|
attention_dropout_rate,
|
|
)
|
|
# feed-forward module definition
|
|
positionwise_layer = PositionwiseFeedForward
|
|
positionwise_layer_args = (
|
|
output_size,
|
|
linear_units,
|
|
dropout_rate,
|
|
activation,
|
|
)
|
|
# convolution module definition
|
|
convolution_layer = ConvolutionModule
|
|
convolution_layer_args = (output_size, cnn_module_kernel, activation,
|
|
cnn_module_norm, causal)
|
|
|
|
self.encoders = torch.nn.ModuleList([
|
|
ConformerEncoderLayer(
|
|
output_size,
|
|
encoder_selfattn_layer(*encoder_selfattn_layer_args),
|
|
positionwise_layer(*positionwise_layer_args),
|
|
positionwise_layer(
|
|
*positionwise_layer_args) if macaron_style else None,
|
|
convolution_layer(
|
|
*convolution_layer_args) if use_cnn_module else None,
|
|
dropout_rate,
|
|
normalize_before,
|
|
concat_after,
|
|
) for _ in range(num_blocks)
|
|
])
|