246 lines
8.8 KiB
Python
Executable File
246 lines
8.8 KiB
Python
Executable File
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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import numpy as np
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from dataclasses import dataclass
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from transformers import Wav2Vec2Model, Wav2Vec2PreTrainedModel
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from transformers.modeling_outputs import BaseModelOutput
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from typing import Optional, Tuple
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from transformers.file_utils import ModelOutput
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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_CONFIG_FOR_DOC = "Wav2Vec2Config"
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_HIDDEN_STATES_START_POSITION = 2
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# the implementation of Wav2Vec2Model is borrowed from https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model
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# initialize our encoder with the pre-trained wav2vec 2.0 weights.
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def _compute_mask_indices(
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shape: Tuple[int, int],
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mask_prob: float,
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mask_length: int,
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attention_mask: Optional[torch.Tensor] = None,
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min_masks: int = 0,
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) -> np.ndarray:
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bsz, all_sz = shape
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mask = np.full((bsz, all_sz), False)
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all_num_mask = int(
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mask_prob * all_sz / float(mask_length)
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+ np.random.rand()
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)
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all_num_mask = max(min_masks, all_num_mask)
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mask_idcs = []
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padding_mask = attention_mask.ne(1) if attention_mask is not None else None
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for i in range(bsz):
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if padding_mask is not None:
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sz = all_sz - padding_mask[i].long().sum().item()
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num_mask = int(
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mask_prob * sz / float(mask_length)
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+ np.random.rand()
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)
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num_mask = max(min_masks, num_mask)
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else:
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sz = all_sz
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num_mask = all_num_mask
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lengths = np.full(num_mask, mask_length)
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if sum(lengths) == 0:
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lengths[0] = min(mask_length, sz - 1)
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min_len = min(lengths)
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if sz - min_len <= num_mask:
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min_len = sz - num_mask - 1
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mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
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mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])])
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mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
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min_len = min([len(m) for m in mask_idcs])
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for i, mask_idc in enumerate(mask_idcs):
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if len(mask_idc) > min_len:
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mask_idc = np.random.choice(mask_idc, min_len, replace=False)
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mask[i, mask_idc] = True
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return mask
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# linear interpolation layer
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def linear_interpolation(features, input_fps, output_fps, output_len=None):
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features = features.transpose(1, 2)
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seq_len = features.shape[2] / float(input_fps)
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if output_len is None:
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output_len = int(seq_len * output_fps)
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output_features = F.interpolate(features, size=output_len, align_corners=True, mode='linear')
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return output_features.transpose(1, 2)
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class Wav2Vec2Model(Wav2Vec2Model):
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def __init__(self, config):
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super().__init__(config)
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self.lm_head = nn.Linear(1024, 32)
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def forward(
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self,
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input_values,
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attention_mask=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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frame_num=None
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):
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self.config.output_attentions = True
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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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hidden_states = self.feature_extractor(input_values)
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hidden_states = hidden_states.transpose(1, 2)
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hidden_states = linear_interpolation(hidden_states, 50, 30, output_len=frame_num)
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if attention_mask is not None:
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output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1))
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attention_mask = torch.zeros(
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hidden_states.shape[:2], dtype=hidden_states.dtype, device=hidden_states.device
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)
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attention_mask[
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(torch.arange(attention_mask.shape[0], device=hidden_states.device), output_lengths - 1)
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] = 1
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attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
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hidden_states = self.feature_projection(hidden_states)[0]
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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 not return_dict:
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return (hidden_states,) + encoder_outputs[1:]
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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hidden_states=encoder_outputs.hidden_states,
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attentions=encoder_outputs.attentions,
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)
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@dataclass
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class SpeechClassifierOutput(ModelOutput):
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loss: Optional[torch.FloatTensor] = None
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logits: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None
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attentions: Optional[Tuple[torch.FloatTensor]] = None
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class Wav2Vec2ClassificationHead(nn.Module):
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"""Head for wav2vec classification task."""
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def __init__(self, config):
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.final_dropout)
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self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
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def forward(self, features, **kwargs):
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x = features
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x = self.dropout(x)
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x = self.dense(x)
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x = torch.tanh(x)
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x = self.dropout(x)
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x = self.out_proj(x)
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return x
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class Wav2Vec2ForSpeechClassification(Wav2Vec2PreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.num_labels = config.num_labels
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self.pooling_mode = config.pooling_mode
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self.config = config
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self.wav2vec2 = Wav2Vec2Model(config)
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self.classifier = Wav2Vec2ClassificationHead(config)
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self.init_weights()
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def freeze_feature_extractor(self):
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self.wav2vec2.feature_extractor._freeze_parameters()
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def merged_strategy(
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self,
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hidden_states,
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mode="mean"
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):
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if mode == "mean":
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outputs = torch.mean(hidden_states, dim=1)
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elif mode == "sum":
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outputs = torch.sum(hidden_states, dim=1)
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elif mode == "max":
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outputs = torch.max(hidden_states, dim=1)[0]
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else:
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raise Exception(
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"The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']")
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return outputs
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def forward(
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self,
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input_values,
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attention_mask=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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labels=None,
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frame_num=None,
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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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outputs = self.wav2vec2(
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input_values,
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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 = outputs[0]
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hidden_states1 = linear_interpolation(hidden_states, 50, 30, output_len=frame_num)
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hidden_states = self.merged_strategy(hidden_states1, mode=self.pooling_mode)
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logits = self.classifier(hidden_states)
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loss = None
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if labels is not None:
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if self.config.problem_type is None:
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if self.num_labels == 1:
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self.config.problem_type = "regression"
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elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
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self.config.problem_type = "single_label_classification"
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else:
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self.config.problem_type = "multi_label_classification"
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if self.config.problem_type == "regression":
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loss_fct = MSELoss()
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loss = loss_fct(logits.view(-1, self.num_labels), labels)
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elif self.config.problem_type == "single_label_classification":
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loss_fct = CrossEntropyLoss()
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loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
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elif self.config.problem_type == "multi_label_classification":
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loss_fct = BCEWithLogitsLoss()
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loss = loss_fct(logits, labels)
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if not return_dict:
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output = (logits,) + outputs[2:]
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return ((loss,) + output) if loss is not None else output
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return SpeechClassifierOutput(
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loss=loss,
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logits=logits,
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hidden_states=hidden_states1,
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attentions=outputs.attentions,
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)
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