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model_handling.py
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model_handling.py
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from transformers import Wav2Vec2PreTrainedModel, Wav2Vec2Model
from torch import nn
import warnings
import torch
from transformers.modeling_outputs import CausalLMOutput
from collections import OrderedDict
_HIDDEN_STATES_START_POSITION = 2
class Wav2Vec2ForCTC(Wav2Vec2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.wav2vec2 = Wav2Vec2Model(config)
self.dropout = nn.Dropout(config.final_dropout)
self.feature_transform = nn.Sequential(OrderedDict([
('linear1', nn.Linear(config.hidden_size, config.hidden_size)),
('bn1', nn.BatchNorm1d(config.hidden_size)),
('activation1', nn.LeakyReLU()),
('drop1', nn.Dropout(config.final_dropout)),
('linear2', nn.Linear(config.hidden_size, config.hidden_size)),
('bn2', nn.BatchNorm1d(config.hidden_size)),
('activation2', nn.LeakyReLU()),
('drop2', nn.Dropout(config.final_dropout)),
('linear3', nn.Linear(config.hidden_size, config.hidden_size)),
('bn3', nn.BatchNorm1d(config.hidden_size)),
('activation3', nn.LeakyReLU()),
('drop3', nn.Dropout(config.final_dropout))
]))
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configuration that "
"does not define the vocabulary size of the language model head. Please "
"instantiate the model as follows: `Wav2Vec2ForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
"or define `vocab_size` of your model's configuration."
)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
self.is_wav2vec_freeze = False
# Initialize weights and apply final processing
self.post_init()
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5."
"Please use the equivalent `freeze_feature_encoder` method instead.",
FutureWarning,
)
self.freeze_feature_encoder()
def freeze_feature_encoder(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
self.wav2vec2.feature_extractor._freeze_parameters()
def freeze_wav2vec(self, is_freeze=True):
"""
Calling this function will disable the gradient computation for the feature extractor so that its parameter
will not be updated during training.
"""
if is_freeze:
self.is_wav2vec_freeze = True
for param in self.wav2vec2.parameters():
param.requires_grad = False
else:
self.is_wav2vec_freeze = False
for param in self.wav2vec2.parameters():
param.requires_grad = True
self.freeze_feature_encoder()
model_total_params = sum(p.numel() for p in self.parameters())
model_total_params_trainable = sum(p.numel() for p in self.parameters() if p.requires_grad)
print("model_total_params: {}\nmodel_total_params_trainable: {}".format(model_total_params,
model_total_params_trainable))
def forward(
self,
input_values,
attention_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
):
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.wav2vec2(
input_values,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
hidden_states = self.dropout(hidden_states)
B, T, F = hidden_states.size()
hidden_states = hidden_states.view(B * T, F)
hidden_states = self.feature_transform(hidden_states)
hidden_states = hidden_states.view(B, T, F)
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
if labels.max() >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
# retrieve loss input_lengths from attention_mask
attention_mask = (
attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long)
)
input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
# assuming that padded tokens are filled with -100
# when not being attended to
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
# ctc_loss doesn't support fp16
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
with torch.backends.cudnn.flags(enabled=False):
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=self.config.pad_token_id,
reduction=self.config.ctc_loss_reduction,
zero_infinity=self.config.ctc_zero_infinity,
)
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)