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#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
import math
import torch
from typing import Optional, Tuple, Union
from funasr_detach.models.transformer.utils.nets_utils import pad_to_len
class TooShortUttError(Exception):
"""Raised when the utt is too short for subsampling.
Args:
message (str): Message for error catch
actual_size (int): the short size that cannot pass the subsampling
limit (int): the limit size for subsampling
"""
def __init__(self, message, actual_size, limit):
"""Construct a TooShortUttError for error handler."""
super().__init__(message)
self.actual_size = actual_size
self.limit = limit
def check_short_utt(ins, size):
"""Check if the utterance is too short for subsampling."""
if isinstance(ins, Conv2dSubsampling2) and size < 3:
return True, 3
if isinstance(ins, Conv2dSubsampling) and size < 7:
return True, 7
if isinstance(ins, Conv2dSubsampling6) and size < 11:
return True, 11
if isinstance(ins, Conv2dSubsampling8) and size < 15:
return True, 15
return False, -1
class RWKVConvInput(torch.nn.Module):
"""Streaming ConvInput module definition.
Args:
input_size: Input size.
conv_size: Convolution size.
subsampling_factor: Subsampling factor.
output_size: Block output dimension.
"""
def __init__(
self,
input_size: int,
conv_size: Union[int, Tuple],
subsampling_factor: int = 4,
conv_kernel_size: int = 3,
output_size: Optional[int] = None,
) -> None:
"""Construct a ConvInput object."""
super().__init__()
if subsampling_factor == 1:
conv_size1, conv_size2, conv_size3 = conv_size
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(
1,
conv_size1,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size1,
conv_size1,
conv_kernel_size,
stride=[1, 2],
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size1,
conv_size2,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size2,
conv_size2,
conv_kernel_size,
stride=[1, 2],
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size2,
conv_size3,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size3,
conv_size3,
conv_kernel_size,
stride=[1, 2],
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
)
output_proj = conv_size3 * ((input_size // 2) // 2)
self.subsampling_factor = 1
self.stride_1 = 1
self.create_new_mask = self.create_new_vgg_mask
else:
conv_size1, conv_size2, conv_size3 = conv_size
kernel_1 = int(subsampling_factor / 2)
self.conv = torch.nn.Sequential(
torch.nn.Conv2d(
1,
conv_size1,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size1,
conv_size1,
conv_kernel_size,
stride=[kernel_1, 2],
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size1,
conv_size2,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size2,
conv_size2,
conv_kernel_size,
stride=[2, 2],
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size2,
conv_size3,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
torch.nn.Conv2d(
conv_size3,
conv_size3,
conv_kernel_size,
stride=1,
padding=(conv_kernel_size - 1) // 2,
),
torch.nn.ReLU(),
)
output_proj = conv_size3 * ((input_size // 2) // 2)
self.subsampling_factor = subsampling_factor
self.create_new_mask = self.create_new_vgg_mask
self.stride_1 = kernel_1
self.min_frame_length = 7
if output_size is not None:
self.output = torch.nn.Linear(output_proj, output_size)
self.output_size = output_size
else:
self.output = None
self.output_size = output_proj
def forward(
self,
x: torch.Tensor,
mask: Optional[torch.Tensor],
chunk_size: Optional[torch.Tensor],
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Encode input sequences.
Args:
x: ConvInput input sequences. (B, T, D_feats)
mask: Mask of input sequences. (B, 1, T)
Returns:
x: ConvInput output sequences. (B, sub(T), D_out)
mask: Mask of output sequences. (B, 1, sub(T))
"""
if mask is not None:
mask = self.create_new_mask(mask)
olens = max(mask.eq(0).sum(1))
b, t, f = x.size()
x = x.unsqueeze(1) # (b. 1. t. f)
if chunk_size is not None:
max_input_length = int(
chunk_size
* self.subsampling_factor
* (math.ceil(float(t) / (chunk_size * self.subsampling_factor)))
)
x = map(lambda inputs: pad_to_len(inputs, max_input_length, 1), x)
x = list(x)
x = torch.stack(x, dim=0)
N_chunks = max_input_length // (chunk_size * self.subsampling_factor)
x = x.view(b * N_chunks, 1, chunk_size * self.subsampling_factor, f)
x = self.conv(x)
_, c, _, f = x.size()
if chunk_size is not None:
x = x.transpose(1, 2).contiguous().view(b, -1, c * f)[:, :olens, :]
else:
x = x.transpose(1, 2).contiguous().view(b, -1, c * f)
if self.output is not None:
x = self.output(x)
return x, mask[:, :olens][:, : x.size(1)]
def create_new_vgg_mask(self, mask: torch.Tensor) -> torch.Tensor:
"""Create a new mask for VGG output sequences.
Args:
mask: Mask of input sequences. (B, T)
Returns:
mask: Mask of output sequences. (B, sub(T))
"""
if self.subsampling_factor > 1:
return mask[:, ::2][:, :: self.stride_1]
else:
return mask
def get_size_before_subsampling(self, size: int) -> int:
"""Return the original size before subsampling for a given size.
Args:
size: Number of frames after subsampling.
Returns:
: Number of frames before subsampling.
"""
return size * self.subsampling_factor
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