mirror of
https://github.com/aigc3d/LAM_Audio2Expression.git
synced 2026-02-05 01:49:23 +08:00
87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
import numpy as np
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import torch
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from transformers import WavLMModel
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from transformers.modeling_outputs import Wav2Vec2BaseModelOutput
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from typing import Optional, Tuple, Union
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import torch.nn.functional as F
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def linear_interpolation(features, output_len: int):
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features = features.transpose(1, 2)
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output_features = F.interpolate(
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features, size=output_len, align_corners=True, mode='linear')
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return output_features.transpose(1, 2)
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# the implementation of Wav2Vec2Model is borrowed from https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model # noqa: E501
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# initialize our encoder with the pre-trained wav2vec 2.0 weights.
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class WavLMModel(WavLMModel):
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def __init__(self, config):
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super().__init__(config)
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def _freeze_wav2vec2_parameters(self, do_freeze: bool = True):
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for param in self.parameters():
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param.requires_grad = (not do_freeze)
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def forward(
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self,
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input_values: Optional[torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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mask_time_indices: Optional[torch.FloatTensor] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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frame_num=None,
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interpolate_pos: int = 0,
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) -> Union[Tuple, Wav2Vec2BaseModelOutput]:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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extract_features = self.feature_extractor(input_values)
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extract_features = extract_features.transpose(1, 2)
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if interpolate_pos == 0:
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extract_features = linear_interpolation(
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extract_features, output_len=frame_num)
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if attention_mask is not None:
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# compute reduced attention_mask corresponding to feature vectors
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attention_mask = self._get_feature_vector_attention_mask(
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extract_features.shape[1], attention_mask, add_adapter=False
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)
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hidden_states, extract_features = self.feature_projection(extract_features)
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hidden_states = self._mask_hidden_states(
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hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask
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)
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encoder_outputs = self.encoder(
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hidden_states,
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attention_mask=attention_mask,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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hidden_states = encoder_outputs[0]
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if interpolate_pos == 1:
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hidden_states = linear_interpolation(
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hidden_states, output_len=frame_num)
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if self.adapter is not None:
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hidden_states = self.adapter(hidden_states)
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if not return_dict:
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return (hidden_states, extract_features) + encoder_outputs[1:]
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return Wav2Vec2BaseModelOutput(
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last_hidden_state=hidden_states,
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extract_features=extract_features,
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hidden_states=encoder_outputs.hidden_states,
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attentions=encoder_outputs.attentions,
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) |