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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 | |