500 lines
19 KiB
Python
500 lines
19 KiB
Python
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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from typing import List
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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import logging
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from funasr.models.scama.chunk_utilis import overlap_chunk
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import numpy as np
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from funasr.train_utils.device_funcs import to_device
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.models.sanm.attention import MultiHeadedAttention, MultiHeadedAttentionSANM
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from funasr.models.transformer.embedding import (
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SinusoidalPositionEncoder,
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StreamSinusoidalPositionEncoder,
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)
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from funasr.models.transformer.layer_norm import LayerNorm
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from funasr.models.transformer.utils.multi_layer_conv import Conv1dLinear
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from funasr.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d
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from funasr.models.transformer.positionwise_feed_forward import (
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PositionwiseFeedForward, # noqa: H301
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)
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from funasr.models.transformer.utils.repeat import repeat
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from funasr.models.transformer.utils.subsampling import Conv2dSubsampling
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from funasr.models.transformer.utils.subsampling import Conv2dSubsampling2
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from funasr.models.transformer.utils.subsampling import Conv2dSubsampling6
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from funasr.models.transformer.utils.subsampling import Conv2dSubsampling8
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from funasr.models.transformer.utils.subsampling import TooShortUttError
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from funasr.models.transformer.utils.subsampling import check_short_utt
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from funasr.models.transformer.utils.mask import subsequent_mask, vad_mask
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from funasr.models.ctc.ctc import CTC
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from funasr.register import tables
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class EncoderLayerSANM(nn.Module):
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def __init__(
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self,
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in_size,
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size,
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self_attn,
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feed_forward,
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dropout_rate,
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normalize_before=True,
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concat_after=False,
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stochastic_depth_rate=0.0,
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):
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"""Construct an EncoderLayer object."""
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super(EncoderLayerSANM, self).__init__()
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self.self_attn = self_attn
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self.feed_forward = feed_forward
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self.norm1 = LayerNorm(in_size)
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self.norm2 = LayerNorm(size)
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self.dropout = nn.Dropout(dropout_rate)
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self.in_size = in_size
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self.size = size
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self.normalize_before = normalize_before
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self.concat_after = concat_after
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if self.concat_after:
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self.concat_linear = nn.Linear(size + size, size)
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self.stochastic_depth_rate = stochastic_depth_rate
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self.dropout_rate = dropout_rate
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def forward(self, x, mask, cache=None, mask_shfit_chunk=None, mask_att_chunk_encoder=None):
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"""Compute encoded features.
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Args:
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x_input (torch.Tensor): Input tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, time).
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cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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skip_layer = False
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# with stochastic depth, residual connection `x + f(x)` becomes
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# `x <- x + 1 / (1 - p) * f(x)` at training time.
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stoch_layer_coeff = 1.0
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if self.training and self.stochastic_depth_rate > 0:
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skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
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stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
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if skip_layer:
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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return x, mask
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residual = x
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if self.normalize_before:
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x = self.norm1(x)
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if self.concat_after:
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x_concat = torch.cat(
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(
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x,
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self.self_attn(
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x,
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mask,
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mask_shfit_chunk=mask_shfit_chunk,
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mask_att_chunk_encoder=mask_att_chunk_encoder,
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),
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),
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dim=-1,
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)
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if self.in_size == self.size:
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x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
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else:
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x = stoch_layer_coeff * self.concat_linear(x_concat)
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else:
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if self.in_size == self.size:
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x = residual + stoch_layer_coeff * self.dropout(
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self.self_attn(
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x,
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mask,
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mask_shfit_chunk=mask_shfit_chunk,
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mask_att_chunk_encoder=mask_att_chunk_encoder,
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)
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)
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else:
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x = stoch_layer_coeff * self.dropout(
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self.self_attn(
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x,
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mask,
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mask_shfit_chunk=mask_shfit_chunk,
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mask_att_chunk_encoder=mask_att_chunk_encoder,
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)
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)
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if not self.normalize_before:
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x = self.norm1(x)
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residual = x
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if self.normalize_before:
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x = self.norm2(x)
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x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
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if not self.normalize_before:
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x = self.norm2(x)
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return x, mask, cache, mask_shfit_chunk, mask_att_chunk_encoder
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def forward_chunk(self, x, cache=None, chunk_size=None, look_back=0):
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"""Compute encoded features.
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Args:
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x_input (torch.Tensor): Input tensor (#batch, time, size).
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mask (torch.Tensor): Mask tensor for the input (#batch, time).
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cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
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Returns:
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torch.Tensor: Output tensor (#batch, time, size).
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torch.Tensor: Mask tensor (#batch, time).
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"""
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residual = x
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if self.normalize_before:
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x = self.norm1(x)
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if self.in_size == self.size:
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attn, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back)
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x = residual + attn
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else:
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x, cache = self.self_attn.forward_chunk(x, cache, chunk_size, look_back)
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if not self.normalize_before:
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x = self.norm1(x)
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residual = x
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if self.normalize_before:
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x = self.norm2(x)
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x = residual + self.feed_forward(x)
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if not self.normalize_before:
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x = self.norm2(x)
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return x, cache
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@tables.register("encoder_classes", "SANMEncoderChunkOpt")
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class SANMEncoderChunkOpt(nn.Module):
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"""
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Author: Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei, Jie Gao, Zhijie Yan, Lei Xie
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SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
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https://arxiv.org/abs/2006.01712
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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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output_size: int = 256,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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attention_dropout_rate: float = 0.0,
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input_layer: Optional[str] = "conv2d",
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pos_enc_class=SinusoidalPositionEncoder,
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normalize_before: bool = True,
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concat_after: bool = False,
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positionwise_layer_type: str = "linear",
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positionwise_conv_kernel_size: int = 1,
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padding_idx: int = -1,
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interctc_layer_idx: List[int] = [],
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interctc_use_conditioning: bool = False,
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kernel_size: int = 11,
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sanm_shfit: int = 0,
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selfattention_layer_type: str = "sanm",
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chunk_size: Union[int, Sequence[int]] = (16,),
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stride: Union[int, Sequence[int]] = (10,),
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pad_left: Union[int, Sequence[int]] = (0,),
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encoder_att_look_back_factor: Union[int, Sequence[int]] = (1,),
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decoder_att_look_back_factor: Union[int, Sequence[int]] = (1,),
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tf2torch_tensor_name_prefix_torch: str = "encoder",
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tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
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):
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super().__init__()
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self._output_size = output_size
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if input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(input_size, output_size),
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torch.nn.LayerNorm(output_size),
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torch.nn.Dropout(dropout_rate),
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torch.nn.ReLU(),
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer == "conv2d":
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self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d2":
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self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d6":
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self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
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elif input_layer == "conv2d8":
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self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
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elif input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
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pos_enc_class(output_size, positional_dropout_rate),
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)
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elif input_layer is None:
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if input_size == output_size:
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self.embed = None
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else:
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self.embed = torch.nn.Linear(input_size, output_size)
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elif input_layer == "pe":
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self.embed = SinusoidalPositionEncoder()
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elif input_layer == "pe_online":
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self.embed = StreamSinusoidalPositionEncoder()
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else:
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raise ValueError("unknown input_layer: " + input_layer)
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self.normalize_before = normalize_before
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if positionwise_layer_type == "linear":
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positionwise_layer = PositionwiseFeedForward
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positionwise_layer_args = (
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output_size,
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linear_units,
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dropout_rate,
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)
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elif positionwise_layer_type == "conv1d":
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positionwise_layer = MultiLayeredConv1d
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positionwise_layer_args = (
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output_size,
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linear_units,
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positionwise_conv_kernel_size,
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dropout_rate,
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)
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elif positionwise_layer_type == "conv1d-linear":
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positionwise_layer = Conv1dLinear
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positionwise_layer_args = (
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output_size,
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linear_units,
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positionwise_conv_kernel_size,
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dropout_rate,
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)
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else:
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raise NotImplementedError("Support only linear or conv1d.")
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if selfattention_layer_type == "selfattn":
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encoder_selfattn_layer = MultiHeadedAttention
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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attention_dropout_rate,
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)
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elif selfattention_layer_type == "sanm":
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encoder_selfattn_layer = MultiHeadedAttentionSANM
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encoder_selfattn_layer_args0 = (
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attention_heads,
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input_size,
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output_size,
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attention_dropout_rate,
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kernel_size,
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sanm_shfit,
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)
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encoder_selfattn_layer_args = (
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attention_heads,
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output_size,
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output_size,
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attention_dropout_rate,
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kernel_size,
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sanm_shfit,
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)
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self.encoders0 = repeat(
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1,
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lambda lnum: EncoderLayerSANM(
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input_size,
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output_size,
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encoder_selfattn_layer(*encoder_selfattn_layer_args0),
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positionwise_layer(*positionwise_layer_args),
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dropout_rate,
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normalize_before,
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concat_after,
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),
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)
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self.encoders = repeat(
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num_blocks - 1,
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lambda lnum: EncoderLayerSANM(
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output_size,
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output_size,
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encoder_selfattn_layer(*encoder_selfattn_layer_args),
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positionwise_layer(*positionwise_layer_args),
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dropout_rate,
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normalize_before,
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concat_after,
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),
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)
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if self.normalize_before:
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self.after_norm = LayerNorm(output_size)
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self.interctc_layer_idx = interctc_layer_idx
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if len(interctc_layer_idx) > 0:
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assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
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self.interctc_use_conditioning = interctc_use_conditioning
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self.conditioning_layer = None
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shfit_fsmn = (kernel_size - 1) // 2
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self.overlap_chunk_cls = overlap_chunk(
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chunk_size=chunk_size,
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stride=stride,
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pad_left=pad_left,
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shfit_fsmn=shfit_fsmn,
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encoder_att_look_back_factor=encoder_att_look_back_factor,
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decoder_att_look_back_factor=decoder_att_look_back_factor,
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)
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self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
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self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
|
||
|
|
|
||
|
|
def output_size(self) -> int:
|
||
|
|
return self._output_size
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
xs_pad: torch.Tensor,
|
||
|
|
ilens: torch.Tensor,
|
||
|
|
prev_states: torch.Tensor = None,
|
||
|
|
ctc: CTC = None,
|
||
|
|
ind: int = 0,
|
||
|
|
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
|
||
|
|
"""Embed positions in tensor.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
xs_pad: input tensor (B, L, D)
|
||
|
|
ilens: input length (B)
|
||
|
|
prev_states: Not to be used now.
|
||
|
|
Returns:
|
||
|
|
position embedded tensor and mask
|
||
|
|
"""
|
||
|
|
masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
|
||
|
|
xs_pad *= self.output_size() ** 0.5
|
||
|
|
if self.embed is None:
|
||
|
|
xs_pad = xs_pad
|
||
|
|
elif (
|
||
|
|
isinstance(self.embed, Conv2dSubsampling)
|
||
|
|
or isinstance(self.embed, Conv2dSubsampling2)
|
||
|
|
or isinstance(self.embed, Conv2dSubsampling6)
|
||
|
|
or isinstance(self.embed, Conv2dSubsampling8)
|
||
|
|
):
|
||
|
|
short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
|
||
|
|
if short_status:
|
||
|
|
raise TooShortUttError(
|
||
|
|
f"has {xs_pad.size(1)} frames and is too short for subsampling "
|
||
|
|
+ f"(it needs more than {limit_size} frames), return empty results",
|
||
|
|
xs_pad.size(1),
|
||
|
|
limit_size,
|
||
|
|
)
|
||
|
|
xs_pad, masks = self.embed(xs_pad, masks)
|
||
|
|
else:
|
||
|
|
xs_pad = self.embed(xs_pad)
|
||
|
|
|
||
|
|
mask_shfit_chunk, mask_att_chunk_encoder = None, None
|
||
|
|
if self.overlap_chunk_cls is not None:
|
||
|
|
ilens = masks.squeeze(1).sum(1)
|
||
|
|
chunk_outs = self.overlap_chunk_cls.gen_chunk_mask(ilens, ind)
|
||
|
|
xs_pad, ilens = self.overlap_chunk_cls.split_chunk(xs_pad, ilens, chunk_outs=chunk_outs)
|
||
|
|
masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
|
||
|
|
mask_shfit_chunk = self.overlap_chunk_cls.get_mask_shfit_chunk(
|
||
|
|
chunk_outs, xs_pad.device, xs_pad.size(0), dtype=xs_pad.dtype
|
||
|
|
)
|
||
|
|
mask_att_chunk_encoder = self.overlap_chunk_cls.get_mask_att_chunk_encoder(
|
||
|
|
chunk_outs, xs_pad.device, xs_pad.size(0), dtype=xs_pad.dtype
|
||
|
|
)
|
||
|
|
|
||
|
|
encoder_outs = self.encoders0(xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder)
|
||
|
|
xs_pad, masks = encoder_outs[0], encoder_outs[1]
|
||
|
|
intermediate_outs = []
|
||
|
|
if len(self.interctc_layer_idx) == 0:
|
||
|
|
encoder_outs = self.encoders(
|
||
|
|
xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder
|
||
|
|
)
|
||
|
|
xs_pad, masks = encoder_outs[0], encoder_outs[1]
|
||
|
|
else:
|
||
|
|
for layer_idx, encoder_layer in enumerate(self.encoders):
|
||
|
|
encoder_outs = encoder_layer(
|
||
|
|
xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder
|
||
|
|
)
|
||
|
|
xs_pad, masks = encoder_outs[0], encoder_outs[1]
|
||
|
|
if layer_idx + 1 in self.interctc_layer_idx:
|
||
|
|
encoder_out = xs_pad
|
||
|
|
|
||
|
|
# intermediate outputs are also normalized
|
||
|
|
if self.normalize_before:
|
||
|
|
encoder_out = self.after_norm(encoder_out)
|
||
|
|
|
||
|
|
intermediate_outs.append((layer_idx + 1, encoder_out))
|
||
|
|
|
||
|
|
if self.interctc_use_conditioning:
|
||
|
|
ctc_out = ctc.softmax(encoder_out)
|
||
|
|
xs_pad = xs_pad + self.conditioning_layer(ctc_out)
|
||
|
|
|
||
|
|
if self.normalize_before:
|
||
|
|
xs_pad = self.after_norm(xs_pad)
|
||
|
|
|
||
|
|
olens = masks.squeeze(1).sum(1)
|
||
|
|
if len(intermediate_outs) > 0:
|
||
|
|
return (xs_pad, intermediate_outs), olens, None
|
||
|
|
return xs_pad, olens, None
|
||
|
|
|
||
|
|
def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}):
|
||
|
|
if len(cache) == 0:
|
||
|
|
return feats
|
||
|
|
cache["feats"] = to_device(cache["feats"], device=feats.device)
|
||
|
|
overlap_feats = torch.cat((cache["feats"], feats), dim=1)
|
||
|
|
cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]) :, :]
|
||
|
|
return overlap_feats
|
||
|
|
|
||
|
|
def forward_chunk(
|
||
|
|
self,
|
||
|
|
xs_pad: torch.Tensor,
|
||
|
|
ilens: torch.Tensor,
|
||
|
|
cache: dict = None,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
is_final = kwargs.get("is_final", False)
|
||
|
|
xs_pad *= self.output_size() ** 0.5
|
||
|
|
if self.embed is None:
|
||
|
|
xs_pad = xs_pad
|
||
|
|
else:
|
||
|
|
xs_pad = self.embed(xs_pad, cache)
|
||
|
|
if cache["tail_chunk"]:
|
||
|
|
xs_pad = to_device(cache["feats"], device=xs_pad.device)
|
||
|
|
else:
|
||
|
|
xs_pad = self._add_overlap_chunk(xs_pad, cache)
|
||
|
|
if cache["opt"] is None:
|
||
|
|
cache_layer_num = len(self.encoders0) + len(self.encoders)
|
||
|
|
new_cache = [None] * cache_layer_num
|
||
|
|
else:
|
||
|
|
new_cache = cache["opt"]
|
||
|
|
|
||
|
|
for layer_idx, encoder_layer in enumerate(self.encoders0):
|
||
|
|
encoder_outs = encoder_layer.forward_chunk(
|
||
|
|
xs_pad, new_cache[layer_idx], cache["chunk_size"], cache["encoder_chunk_look_back"]
|
||
|
|
)
|
||
|
|
xs_pad, new_cache[0] = encoder_outs[0], encoder_outs[1]
|
||
|
|
|
||
|
|
for layer_idx, encoder_layer in enumerate(self.encoders):
|
||
|
|
encoder_outs = encoder_layer.forward_chunk(
|
||
|
|
xs_pad,
|
||
|
|
new_cache[layer_idx + len(self.encoders0)],
|
||
|
|
cache["chunk_size"],
|
||
|
|
cache["encoder_chunk_look_back"],
|
||
|
|
)
|
||
|
|
xs_pad, new_cache[layer_idx + len(self.encoders0)] = encoder_outs[0], encoder_outs[1]
|
||
|
|
|
||
|
|
if self.normalize_before:
|
||
|
|
xs_pad = self.after_norm(xs_pad)
|
||
|
|
if cache["encoder_chunk_look_back"] > 0 or cache["encoder_chunk_look_back"] == -1:
|
||
|
|
cache["opt"] = new_cache
|
||
|
|
|
||
|
|
return xs_pad, ilens, None
|