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import pdb
import torch
import torch.nn.functional as F
import numpy as np
from transformers import Wav2Vec2Model,Wav2Vec2Config
from transformers.modeling_outputs import BaseModelOutput
from typing import Optional, Tuple
_CONFIG_FOR_DOC = "Wav2Vec2Config"
# the implementation of Wav2Vec2Model is borrowed from https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model
# initialize our encoder with the pre-trained wav2vec 2.0 weights.
def _compute_mask_indices(
shape: Tuple[int, int],
mask_prob: float,
mask_length: int,
attention_mask: Optional[torch.Tensor] = None,
min_masks: int = 0,
) -> np.ndarray:
bsz, all_sz = shape
mask = np.full((bsz, all_sz), False)
all_num_mask = int(
mask_prob * all_sz / float(mask_length)
+ np.random.rand()
)
all_num_mask = max(min_masks, all_num_mask)
mask_idcs = []
padding_mask = attention_mask.ne(1) if attention_mask is not None else None
for i in range(bsz):
if padding_mask is not None:
sz = all_sz - padding_mask[i].long().sum().item()
num_mask = int(
mask_prob * sz / float(mask_length)
+ np.random.rand()
)
num_mask = max(min_masks, num_mask)
else:
sz = all_sz
num_mask = all_num_mask
lengths = np.full(num_mask, mask_length)
if sum(lengths) == 0:
lengths[0] = min(mask_length, sz - 1)
min_len = min(lengths)
if sz - min_len <= num_mask:
min_len = sz - num_mask - 1
mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])])
mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
min_len = min([len(m) for m in mask_idcs])
for i, mask_idc in enumerate(mask_idcs):
if len(mask_idc) > min_len:
mask_idc = np.random.choice(mask_idc, min_len, replace=False)
mask[i, mask_idc] = True
return mask
# linear interpolation layer
def linear_interpolation(features, input_fps, output_fps, output_len=None):
features = features.transpose(1, 2)
seq_len = features.shape[2] / float(input_fps)
if output_len is None:
output_len = int(seq_len * output_fps)
output_features = F.interpolate(features,size=output_len,align_corners=True,mode='linear')
return output_features.transpose(1, 2)
class Wav2Vec2Model(Wav2Vec2Model):
def __init__(self, config):
super().__init__(config)
def forward(
self,
input_values,
dataset,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
frame_num=None
):
self.config.output_attentions = True
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
hidden_states = self.feature_extractor(input_values)
hidden_states = hidden_states.transpose(1, 2)
if dataset == "BIWI":
# cut audio feature
if hidden_states.shape[1]%2 != 0:
hidden_states = hidden_states[:, :-1]
if frame_num and hidden_states.shape[1]>frame_num*2:
hidden_states = hidden_states[:, :frame_num*2]
elif dataset == "vocaset":
hidden_states = linear_interpolation(hidden_states, 50, 30,output_len=frame_num)
elif dataset == "multi":
if hidden_states.shape[1] % 2 != 0:
hidden_states = hidden_states[:, :-1]
if frame_num!=None:
if hidden_states.shape[1]//2<frame_num:
hidden_states = linear_interpolation(hidden_states, 50, 30, output_len=int(frame_num*2))
# if frame_num:
# hidden_states = linear_interpolation(hidden_states, 50, 30, output_len=frame_num)
# else:
# if hidden_states.shape[1] % 2 != 0:
# hidden_states = hidden_states[:, :-1]
# # if frame_num and hidden_states.shape[1] > frame_num * 2:
# # hidden_states = hidden_states[:, :frame_num * 2]
if attention_mask is not None:
output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1))
attention_mask = torch.zeros(
hidden_states.shape[:2], dtype=hidden_states.dtype, device=hidden_states.device
)
attention_mask[
(torch.arange(attention_mask.shape[0], device=hidden_states.device), output_lengths - 1)
] = 1
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
hidden_states = self.feature_projection(hidden_states)
if self.config.apply_spec_augment and self.training:
batch_size, sequence_length, hidden_size = hidden_states.size()
if self.config.mask_time_prob > 0:
mask_time_indices = _compute_mask_indices(
(batch_size, sequence_length),
self.config.mask_time_prob,
self.config.mask_time_length,
attention_mask=attention_mask,
min_masks=2,
)
hidden_states[torch.from_numpy(mask_time_indices)] = self.masked_spec_embed.to(hidden_states.dtype)
if self.config.mask_feature_prob > 0:
mask_feature_indices = _compute_mask_indices(
(batch_size, hidden_size),
self.config.mask_feature_prob,
self.config.mask_feature_length,
)
mask_feature_indices = torch.from_numpy(mask_feature_indices).to(hidden_states.device)
hidden_states[mask_feature_indices[:, None].expand(-1, sequence_length, -1)] = 0
encoder_outputs = self.encoder(
hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = encoder_outputs[0]
if not return_dict:
return (hidden_states,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
) |