Spaces:
Sleeping
Sleeping
File size: 10,707 Bytes
0874d87 5fc7eb1 0874d87 5fc7eb1 0874d87 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 |
import pytorch_lightning as pl
import torch
from torchmetrics import Accuracy, Precision, Recall, F1Score
from transformers import Wav2Vec2Model, Wav2Vec2ForSequenceClassification
import torch.nn.functional as F
from models.lora import LinearWithLoRA, LoRALayer
class Wav2Vec2Classifier(pl.LightningModule):
def __init__(self, num_classes, optimizer_cfg = "Adam", l1_lambda=0.0):
super(Wav2Vec2Classifier, self).__init__()
self.save_hyperparameters()
# Wav2Vec2 backbone
# self.wav2vec2 = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
self.wav2vec2 = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-large-xlsr-53")
# trying without the need to fine tune it
for param in self.wav2vec2.parameters():
param.requires_grad = False
# Classification head
self.classifier = torch.nn.Linear(self.wav2vec2.config.hidden_size, num_classes)
# Metrics
self.accuracy = Accuracy(task="multiclass", num_classes=num_classes)
self.precision = Precision(task="multiclass", num_classes=num_classes)
self.recall = Recall(task="multiclass", num_classes=num_classes)
self.f1 = F1Score(task="multiclass", num_classes=num_classes)
self.l1_lambda = l1_lambda
if optimizer_cfg is not None:
optimizer_name = optimizer_cfg.name
optimizer_lr = optimizer_cfg.lr
optimizer_weight_decay = optimizer_cfg.weight_decay
if optimizer_name == 'Adam':
self.optimizer = torch.optim.Adam(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
elif optimizer_name == 'SGD':
self.optimizer = torch.optim.SGD(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
else:
raise ValueError(f"Unsupported optimizer: {optimizer_name}")
else:
self.optimizer = None
def forward(self, x, attention_mask=None):
# Debug input shape
# Ensure input shape is [batch_size, sequence_length]
if x.dim() > 2:
x = x.squeeze(-1) # Remove unnecessary dimensions if present
# Pass through Wav2Vec2 backbone
output = self.wav2vec2(x, attention_mask=attention_mask)
x = output.last_hidden_state
# Classification head
x = torch.mean(x, dim=1) # Pooling
logits = self.classifier(x)
return logits
def training_step(self, batch, batch_idx):
x, attention_mask, y = batch
# Forward pass
logits = self(x, attention_mask=attention_mask)
# Compute loss
loss = F.cross_entropy(logits, y)
# Add L1 regularization if specified
l1_norm = sum(param.abs().sum() for param in self.parameters())
loss += self.l1_lambda * l1_norm
# Log metrics
self.log("train_loss", loss, prog_bar=True, logger=True)
return loss
def validation_step(self, batch, batch_idx):
x, attention_mask, y = batch # Unpack batch
# Forward pass
logits = self(x, attention_mask=attention_mask)
# Compute loss and metrics
loss = F.cross_entropy(logits, y)
preds = torch.argmax(logits, dim=1)
accuracy = self.accuracy(preds, y)
precision = self.precision(preds, y)
recall = self.recall(preds, y)
f1 = self.f1(preds, y)
# Log metrics
self.log("val_loss", loss, prog_bar=True, logger=True)
self.log("val_acc", accuracy, prog_bar=True, logger=True)
self.log("val_precision", precision, prog_bar=True, logger=True)
self.log("val_recall", recall, prog_bar=True, logger=True)
self.log("val_f1", f1, prog_bar=True, logger=True)
return loss
def test_step(self, batch, batch_idx):
x, attention_mask, y = batch # Unpack batch
# Forward pass
logits = self(x, attention_mask=attention_mask)
# Compute loss and metrics
loss = F.cross_entropy(logits, y)
preds = torch.argmax(logits, dim=1)
accuracy = self.accuracy(preds, y)
precision = self.precision(preds, y)
recall = self.recall(preds, y)
f1 = self.f1(preds, y)
# Log metrics
self.log("test_loss", loss, prog_bar=True, logger=True)
self.log("test_acc", accuracy, prog_bar=True, logger=True)
self.log("test_precision", precision, prog_bar=True, logger=True)
self.log("test_recall", recall, prog_bar=True, logger=True)
self.log("test_f1", f1, prog_bar=True, logger=True)
return {"test_loss": loss, "test_accuracy": accuracy}
def configure_optimizers(self):
optimizer = self.optimizer
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.2, patience=20, min_lr=5e-5)
return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "val_loss"}
class Wav2Vec2EmotionClassifier(pl.LightningModule):
def __init__(self, num_classes, learning_rate=1e-4, freeze_base=False, optimizer_cfg="AdamW"):
super(Wav2Vec2EmotionClassifier, self).__init__()
self.save_hyperparameters()
# Load a pre-trained Wav2Vec2 model optimized for emotion recognition
self.model = Wav2Vec2ForSequenceClassification.from_pretrained(
"audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim",
num_labels=num_classes,
)
# Optionally freeze the Wav2Vec2 base layers
if freeze_base:
for param in self.model.wav2vec2.parameters():
param.requires_grad = False
# Metrics
self.accuracy = Accuracy(task="multiclass", num_classes=num_classes)
self.precision = Precision(task="multiclass", num_classes=num_classes)
self.recall = Recall(task="multiclass", num_classes=num_classes)
self.f1 = F1Score(task="multiclass", num_classes=num_classes)
self.learning_rate = learning_rate
if optimizer_cfg is not None:
optimizer_name = optimizer_cfg['name']
optimizer_lr = optimizer_cfg['lr']
optimizer_weight_decay = optimizer_cfg['weight_decay']
if optimizer_name == 'Adam':
self.optimizer = torch.optim.Adam(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
elif optimizer_name == 'SGD':
self.optimizer = torch.optim.SGD(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
elif optimizer_name == 'AdamW':
self.optimizer = torch.optim.AdamW(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
else:
raise ValueError(f"Unsupported optimizer: {optimizer_name}")
else:
self.optimizer = None
# Apply LoRA
low_rank = 8
lora_alpha = 16
self.apply_lora(low_rank, lora_alpha)
def apply_lora(self, rank, alpha):
# Replace specific linear layers with LinearWithLoRA
for layer in self.model.wav2vec2.encoder.layers:
layer.attention.q_proj = LinearWithLoRA(layer.attention.q_proj, rank, alpha)
layer.attention.k_proj = LinearWithLoRA(layer.attention.k_proj, rank, alpha)
layer.attention.v_proj = LinearWithLoRA(layer.attention.v_proj, rank, alpha)
layer.attention.out_proj = LinearWithLoRA(layer.attention.out_proj, rank, alpha)
layer.feed_forward.intermediate_dense = LinearWithLoRA(layer.feed_forward.intermediate_dense, rank, alpha)
layer.feed_forward.output_dense = LinearWithLoRA(layer.feed_forward.output_dense, rank, alpha)
def state_dict(self, *args, **kwargs):
# Save only LoRA and classifier/projector parameters
state = super().state_dict(*args, **kwargs)
return {k: v for k, v in state.items() if "lora" in k or "classifier" in k or "projector" in k}
def load_state_dict(self, state_dict, strict=True):
missing_keys, unexpected_keys = super().load_state_dict(state_dict, strict=False)
if missing_keys or unexpected_keys:
print(f"Missing keys: {missing_keys}")
print(f"Unexpected keys: {unexpected_keys}")
def forward(self, x, attention_mask=None):
return self.model(x, attention_mask=attention_mask).logits
def training_step(self, batch, batch_idx):
x, attention_mask, y = batch
# Forward pass
logits = self(x, attention_mask=attention_mask)
# Compute loss
loss = F.cross_entropy(logits, y)
# Log training loss
self.log("train_loss", loss, prog_bar=True, logger=True)
return loss
def validation_step(self, batch, batch_idx):
x, attention_mask, y = batch
# Forward pass
logits = self(x, attention_mask=attention_mask)
# Compute loss and metrics
loss = F.cross_entropy(logits, y)
preds = torch.argmax(logits, dim=1)
accuracy = self.accuracy(preds, y)
precision = self.precision(preds, y)
recall = self.recall(preds, y)
f1 = self.f1(preds, y)
# Log metrics
self.log("val_loss", loss, prog_bar=True, logger=True)
self.log("val_acc", accuracy, prog_bar=True, logger=True)
self.log("val_precision", precision, prog_bar=True, logger=True)
self.log("val_recall", recall, prog_bar=True, logger=True)
self.log("val_f1", f1, prog_bar=True, logger=True)
return loss
def test_step(self, batch, batch_idx):
x, attention_mask, y = batch
# Forward pass
logits = self(x, attention_mask=attention_mask)
# Compute loss and metrics
loss = F.cross_entropy(logits, y)
preds = torch.argmax(logits, dim=1)
accuracy = self.accuracy(preds, y)
precision = self.precision(preds, y)
recall = self.recall(preds, y)
f1 = self.f1(preds, y)
# Log metrics
self.log("test_loss", loss, prog_bar=True, logger=True)
self.log("test_acc", accuracy, prog_bar=True, logger=True)
self.log("test_precision", precision, prog_bar=True, logger=True)
self.log("test_recall", recall, prog_bar=True, logger=True)
self.log("test_f1", f1, prog_bar=True, logger=True)
return {"test_loss": loss, "test_accuracy": accuracy}
def configure_optimizers(self):
optimizer = self.optimizer
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.2, patience=20, min_lr=5e-5)
return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "val_loss"} |