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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
import torch.nn as nn
from functools import partial
import torch.nn.functional as F
from typing import Callable, Dict
from funasr_detach.models.emotion2vec.fairseq_modules import (
LayerNorm,
SamePad,
TransposeLast,
ConvFeatureExtractionModel,
)
from funasr_detach.models.emotion2vec.modules import Modality, BlockEncoder, Decoder1d
from funasr_detach.models.emotion2vec.base import (
ModalitySpecificEncoder,
get_alibi_bias,
)
class AudioEncoder(ModalitySpecificEncoder):
def __init__(
self,
modality_cfg,
embed_dim: int,
make_block: Callable[[float], nn.ModuleList],
norm_layer: Callable[[int], nn.LayerNorm],
layer_norm_first: bool,
alibi_biases: Dict,
):
self.feature_enc_layers = eval(modality_cfg.feature_encoder_spec)
feature_embed_dim = self.feature_enc_layers[-1][0]
local_encoder = ConvFeatureExtractionModel(
conv_layers=self.feature_enc_layers,
dropout=0.0,
mode=modality_cfg.extractor_mode,
conv_bias=False,
)
project_features = nn.Sequential(
TransposeLast(),
nn.LayerNorm(feature_embed_dim),
nn.Linear(feature_embed_dim, embed_dim),
)
num_pos_layers = modality_cfg.conv_pos_depth
k = max(3, modality_cfg.conv_pos_width // num_pos_layers)
positional_encoder = nn.Sequential(
TransposeLast(),
*[
nn.Sequential(
nn.Conv1d(
embed_dim,
embed_dim,
kernel_size=k,
padding=k // 2,
groups=modality_cfg.conv_pos_groups,
),
SamePad(k),
TransposeLast(),
LayerNorm(embed_dim, elementwise_affine=False),
TransposeLast(),
nn.GELU(),
)
for _ in range(num_pos_layers)
],
TransposeLast(),
)
if modality_cfg.conv_pos_pre_ln:
positional_encoder = nn.Sequential(LayerNorm(embed_dim), positional_encoder)
dpr = np.linspace(
modality_cfg.start_drop_path_rate,
modality_cfg.end_drop_path_rate,
modality_cfg.prenet_depth,
)
context_encoder = BlockEncoder(
nn.ModuleList(make_block(dpr[i]) for i in range(modality_cfg.prenet_depth)),
norm_layer(embed_dim) if not layer_norm_first else None,
layer_norm_first,
modality_cfg.prenet_layerdrop,
modality_cfg.prenet_dropout,
)
decoder = (
Decoder1d(modality_cfg.decoder, embed_dim)
if modality_cfg.decoder is not None
else None
)
alibi_bias_fn = partial(get_alibi_bias, alibi_biases=alibi_biases)
super().__init__(
modality_cfg=modality_cfg,
embed_dim=embed_dim,
local_encoder=local_encoder,
project_features=project_features,
fixed_positional_encoder=None,
relative_positional_encoder=positional_encoder,
context_encoder=context_encoder,
decoder=decoder,
get_alibi_bias=alibi_bias_fn,
)
def convert_padding_mask(self, x, padding_mask):
def get_feat_extract_output_lengths(input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers
"""
def _conv_out_length(input_length, kernel_size, stride):
return torch.floor((input_length - kernel_size) / stride + 1)
for i in range(len(self.feature_enc_layers)):
input_lengths = _conv_out_length(
input_lengths,
self.feature_enc_layers[i][1],
self.feature_enc_layers[i][2],
)
return input_lengths.to(torch.long)
if padding_mask is not None:
input_lengths = (1 - padding_mask.long()).sum(-1)
# apply conv formula to get real output_lengths
output_lengths = get_feat_extract_output_lengths(input_lengths)
if padding_mask.any():
padding_mask = torch.zeros(x.shape[:2], dtype=x.dtype, device=x.device)
# these two operations makes sure that all values
# before the output lengths indices are attended to
padding_mask[
(
torch.arange(padding_mask.shape[0], device=padding_mask.device),
output_lengths - 1,
)
] = 1
padding_mask = (
1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])
).bool()
else:
padding_mask = torch.zeros(
x.shape[:2], dtype=torch.bool, device=x.device
)
return padding_mask
def reset_parameters(self):
super().reset_parameters()
for mod in self.project_features.children():
if isinstance(mod, nn.Linear):
mod.reset_parameters()
if self.decoder is not None:
self.decoder.reset_parameters()
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