RMSnow's picture
add backend inference and inferface output
0883aa1
raw
history blame
22.1 kB
# This module is from [WeNet](https://github.com/wenet-e2e/wenet).
# ## Citations
# ```bibtex
# @inproceedings{yao2021wenet,
# title={WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit},
# author={Yao, Zhuoyuan and Wu, Di and Wang, Xiong and Zhang, Binbin and Yu, Fan and Yang, Chao and Peng, Zhendong and Chen, Xiaoyu and Xie, Lei and Lei, Xin},
# booktitle={Proc. Interspeech},
# year={2021},
# address={Brno, Czech Republic },
# organization={IEEE}
# }
# @article{zhang2022wenet,
# title={WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit},
# author={Zhang, Binbin and Wu, Di and Peng, Zhendong and Song, Xingchen and Yao, Zhuoyuan and Lv, Hang and Xie, Lei and Yang, Chao and Pan, Fuping and Niu, Jianwei},
# journal={arXiv preprint arXiv:2203.15455},
# year={2022}
# }
#
import torch
import torch.nn as nn
from typing import Tuple, Union, Optional, List
from modules.wenet_extractor.squeezeformer.subsampling import (
DepthwiseConv2dSubsampling4,
TimeReductionLayer1D,
TimeReductionLayer2D,
TimeReductionLayerStream,
)
from modules.wenet_extractor.squeezeformer.encoder_layer import (
SqueezeformerEncoderLayer,
)
from modules.wenet_extractor.transformer.embedding import RelPositionalEncoding
from modules.wenet_extractor.transformer.attention import MultiHeadedAttention
from modules.wenet_extractor.squeezeformer.attention import (
RelPositionMultiHeadedAttention,
)
from modules.wenet_extractor.squeezeformer.positionwise_feed_forward import (
PositionwiseFeedForward,
)
from modules.wenet_extractor.squeezeformer.convolution import ConvolutionModule
from modules.wenet_extractor.utils.mask import make_pad_mask, add_optional_chunk_mask
from modules.wenet_extractor.utils.common import get_activation
class SqueezeformerEncoder(nn.Module):
def __init__(
self,
input_size: int = 80,
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",
do_rel_shift: bool = True,
feed_forward_dropout_rate: float = 0.1,
attention_dropout_rate: float = 0.1,
cnn_module_kernel: int = 31,
cnn_norm_type: str = "batch_norm",
dropout: float = 0.1,
causal: bool = False,
adaptive_scale: bool = True,
activation_type: str = "swish",
init_weights: bool = True,
global_cmvn: torch.nn.Module = 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.
do_rel_shift (bool): Whether to do relative shift
operation on rel-attention module.
cnn_module_kernel (int): Kernel size of CNN module.
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.
adaptive_scale (bool): Whether to use adaptive scale.
init_weights (bool): Whether to initialize weights.
causal (bool): whether to use causal convolution or not.
"""
super(SqueezeformerEncoder, self).__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
self.pos_enc_layer_type = pos_enc_layer_type
activation = get_activation(activation_type)
# self-attention module definition
if pos_enc_layer_type != "rel_pos":
encoder_selfattn_layer = MultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
output_size,
attention_dropout_rate,
)
else:
encoder_selfattn_layer = RelPositionMultiHeadedAttention
encoder_selfattn_layer_args = (
attention_heads,
encoder_dim,
attention_dropout_rate,
do_rel_shift,
adaptive_scale,
init_weights,
)
# 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 = nn.LayerNorm(encoder_dim)
self.encoders = torch.nn.ModuleList(
[
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 = nn.Linear(encoder_dim, encoder_dim)
self.final_proj = None
if output_size != encoder_dim:
self.final_proj = nn.Linear(encoder_dim, output_size)
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]:
T = xs.size(1)
masks = ~make_pad_mask(xs_lens, T).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,
)
xs_lens = mask_pad.squeeze(1).sum(1)
xs = self.preln(xs)
recover_activations: List[
Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.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 = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2)
xs = self.time_recover_layer(xs)
recoverd_t = recover_tensor.size(1)
xs = recover_tensor + xs[:, :recoverd_t, :].contiguous()
chunk_masks = recover_chunk_masks
pos_emb = recover_pos_emb
mask_pad = recover_mask_pad
xs = xs.masked_fill(~mask_pad[:, 0, :].unsqueeze(-1), 0.0)
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: torch.Tensor,
offset: int,
required_cache_size: int,
att_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
cnn_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
att_mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
""" Forward just one chunk
Args:
xs (torch.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 (torch.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 (torch.Tensor): cache tensor for cnn_module in conformer,
(elayers, b=1, hidden-dim, cache_t2), where
`cache_t2 == cnn.lorder - 1`
Returns:
torch.Tensor: output of current input xs,
with shape (b=1, chunk_size, hidden-dim).
torch.Tensor: new attention cache required for next chunk, with
dynamic shape (elayers, head, ?, d_k * 2)
depending on required_cache_size.
torch.Tensor: new conformer cnn cache required for next chunk, with
same shape as the original 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)
# NOTE(xcsong): Before embed, shape(xs) is (b=1, time, mel-dim)
xs, pos_emb, _ = self.embed(xs, tmp_masks, offset)
# NOTE(xcsong): After embed, shape(xs) is (b=1, chunk_size, hidden-dim)
elayers, cache_t1 = att_cache.size(0), att_cache.size(2)
chunk_size = xs.size(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 = torch.ones(1, xs.size(1), device=xs.device, dtype=torch.bool)
mask_pad = mask_pad.unsqueeze(1)
max_att_len: int = 0
recover_activations: List[
Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
] = []
index = 0
xs_lens = torch.tensor([xs.size(1)], device=xs.device, dtype=torch.int)
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 = xs.unsqueeze(2).repeat(1, 1, 2, 1).flatten(1, 2)
xs = self.time_recover_layer(xs)
recoverd_t = recover_tensor.size(1)
xs = recover_tensor + xs[:, :recoverd_t, :].contiguous()
att_mask = recover_att_mask
pos_emb = recover_pos_emb
mask_pad = recover_mask_pad
if att_mask.size(1) != 0:
xs = xs.masked_fill(~att_mask[:, 0, :].unsqueeze(-1), 0.0)
factor = self.calculate_downsampling_factor(i)
xs, _, new_att_cache, new_cnn_cache = layer(
xs,
att_mask,
pos_emb,
att_cache=att_cache[i : i + 1][:, :, ::factor, :][
:, :, : pos_emb.size(1) - xs.size(1), :
]
if elayers > 0
else att_cache[:, :, ::factor, :],
cnn_cache=cnn_cache[i] if cnn_cache.size(0) > 0 else cnn_cache,
)
# 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.unsqueeze(3).repeat(1, 1, 1, factor, 1).flatten(2, 3)
)
if i == 0:
# record length for the first block as max length
max_att_len = cached_att.size(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 = torch.cat(r_att_cache, dim=0)
# NOTE(xcsong): shape(r_cnn_cache) is (e, b=1, hidden-dim, cache_t2)
r_cnn_cache = torch.cat(r_cnn_cache, dim=0)
if self.final_proj is not None:
xs = self.final_proj(xs)
return (xs, r_att_cache, r_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)
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0), device=xs.device)
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, att_cache, cnn_cache) = self.forward_chunk(
chunk_xs, offset, required_cache_size, att_cache, cnn_cache
)
outputs.append(y)
offset += y.size(1)
ys = torch.cat(outputs, 1)
masks = torch.ones((1, 1, ys.size(1)), device=ys.device, dtype=torch.bool)
return ys, masks