#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Subsampling layer definition."""
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import numpy as np
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import torch
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import torch.nn.functional as F
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from funasr.modules.embedding import PositionalEncoding
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import logging
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from funasr.modules.streaming_utils.utils import sequence_mask
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from funasr.modules.nets_utils import sub_factor_to_params, pad_to_len
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from typing import Optional, Tuple, Union
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import math
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class TooShortUttError(Exception):
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"""Raised when the utt is too short for subsampling.
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Args:
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message (str): Message for error catch
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actual_size (int): the short size that cannot pass the subsampling
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limit (int): the limit size for subsampling
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"""
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def __init__(self, message, actual_size, limit):
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"""Construct a TooShortUttError for error handler."""
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super().__init__(message)
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self.actual_size = actual_size
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self.limit = limit
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def check_short_utt(ins, size):
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"""Check if the utterance is too short for subsampling."""
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if isinstance(ins, Conv2dSubsampling2) and size < 3:
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return True, 3
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if isinstance(ins, Conv2dSubsampling) and size < 7:
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return True, 7
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if isinstance(ins, Conv2dSubsampling6) and size < 11:
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return True, 11
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if isinstance(ins, Conv2dSubsampling8) and size < 15:
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return True, 15
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return False, -1
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class RWKVConvInput(torch.nn.Module):
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"""Streaming ConvInput module definition.
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Args:
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input_size: Input size.
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conv_size: Convolution size.
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subsampling_factor: Subsampling factor.
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output_size: Block output dimension.
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"""
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def __init__(
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self,
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input_size: int,
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conv_size: Union[int, Tuple],
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subsampling_factor: int = 4,
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conv_kernel_size: int = 3,
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output_size: Optional[int] = None,
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) -> None:
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"""Construct a ConvInput object."""
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super().__init__()
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if subsampling_factor == 1:
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conv_size1, conv_size2, conv_size3 = conv_size
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, conv_size1, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size1, conv_size1, conv_kernel_size, stride=[1, 2], padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size1, conv_size2, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size2, conv_size2, conv_kernel_size, stride=[1, 2], padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size2, conv_size3, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size3, conv_size3, conv_kernel_size, stride=[1, 2], padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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)
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output_proj = conv_size3 * ((input_size // 2) // 2)
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self.subsampling_factor = 1
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self.stride_1 = 1
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self.create_new_mask = self.create_new_vgg_mask
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else:
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conv_size1, conv_size2, conv_size3 = conv_size
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kernel_1 = int(subsampling_factor / 2)
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self.conv = torch.nn.Sequential(
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torch.nn.Conv2d(1, conv_size1, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size1, conv_size1, conv_kernel_size, stride=[kernel_1, 2], padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size1, conv_size2, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size2, conv_size2, conv_kernel_size, stride=[2, 2], padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size2, conv_size3, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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torch.nn.Conv2d(conv_size3, conv_size3, conv_kernel_size, stride=1, padding=(conv_kernel_size-1)//2),
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torch.nn.ReLU(),
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)
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output_proj = conv_size3 * ((input_size // 2) // 2)
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self.subsampling_factor = subsampling_factor
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self.create_new_mask = self.create_new_vgg_mask
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self.stride_1 = kernel_1
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self.min_frame_length = 7
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if output_size is not None:
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self.output = torch.nn.Linear(output_proj, output_size)
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self.output_size = output_size
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else:
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self.output = None
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self.output_size = output_proj
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def forward(
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self, x: torch.Tensor, mask: Optional[torch.Tensor], chunk_size: Optional[torch.Tensor]
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Encode input sequences.
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Args:
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x: ConvInput input sequences. (B, T, D_feats)
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mask: Mask of input sequences. (B, 1, T)
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Returns:
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x: ConvInput output sequences. (B, sub(T), D_out)
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mask: Mask of output sequences. (B, 1, sub(T))
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"""
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if mask is not None:
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mask = self.create_new_mask(mask)
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olens = max(mask.eq(0).sum(1))
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b, t, f = x.size()
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x = x.unsqueeze(1) # (b. 1. t. f)
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if chunk_size is not None:
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max_input_length = int(
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chunk_size * self.subsampling_factor * (math.ceil(float(t) / (chunk_size * self.subsampling_factor) ))
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)
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x = map(lambda inputs: pad_to_len(inputs, max_input_length, 1), x)
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x = list(x)
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x = torch.stack(x, dim=0)
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N_chunks = max_input_length // ( chunk_size * self.subsampling_factor)
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x = x.view(b * N_chunks, 1, chunk_size * self.subsampling_factor, f)
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x = self.conv(x)
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_, c, _, f = x.size()
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if chunk_size is not None:
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x = x.transpose(1, 2).contiguous().view(b, -1, c * f)[:,:olens,:]
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else:
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x = x.transpose(1, 2).contiguous().view(b, -1, c * f)
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if self.output is not None:
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x = self.output(x)
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return x, mask[:,:olens][:,:x.size(1)]
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def create_new_vgg_mask(self, mask: torch.Tensor) -> torch.Tensor:
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"""Create a new mask for VGG output sequences.
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Args:
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mask: Mask of input sequences. (B, T)
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Returns:
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mask: Mask of output sequences. (B, sub(T))
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"""
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if self.subsampling_factor > 1:
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return mask[:, ::2][:, ::self.stride_1]
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else:
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return mask
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def get_size_before_subsampling(self, size: int) -> int:
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"""Return the original size before subsampling for a given size.
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Args:
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size: Number of frames after subsampling.
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Returns:
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: Number of frames before subsampling.
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"""
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return size * self.subsampling_factor
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