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from typing import Any
import pandas as pd
from sklearn.model_selection import train_test_split
from transformers import (
AutoFeatureExtractor,
AutoModelForAudioClassification,
TrainingArguments,
Trainer,
ASTConfig,
ASTFeatureExtractor,
ASTForAudioClassification,
)
import torch
from torch import nn
from models.training_environment import TrainingEnvironment
from preprocessing.pipelines import WaveformTrainingPipeline
from preprocessing.dataset import (
DanceDataModule,
HuggingFaceDatasetWrapper,
get_datasets,
)
from preprocessing.dataset import get_music4dance_examples
from .utils import get_id_label_mapping, compute_hf_metrics
import pytorch_lightning as pl
from pytorch_lightning import callbacks as cb
MODEL_CHECKPOINT = "MIT/ast-finetuned-audioset-10-10-0.4593"
class AST(nn.Module):
def __init__(self, labels, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
id2label, label2id = get_id_label_mapping(labels)
config = ASTConfig(
hidden_size=256,
num_hidden_layers=6,
num_attention_heads=4,
id2label=id2label,
label2id=label2id,
num_labels=len(label2id),
ignore_mismatched_sizes=True,
)
self.model = ASTForAudioClassification(config)
def forward(self, x):
return self.model(x).logits
# TODO: Remove waveform normalization from ASTFeatureExtractor.
# Find correct mean and std dev
# Find correct max length
class ASTExtractorWrapper:
def __init__(self, sampling_rate=16000, return_tensors="pt") -> None:
max_length = 1024
self.extractor = ASTFeatureExtractor(do_normalize=False, max_length=max_length)
self.sampling_rate = sampling_rate
self.return_tensors = return_tensors
self.waveform_pipeline = WaveformTrainingPipeline() # TODO configure from yaml
def __call__(self, x) -> Any:
x = self.waveform_pipeline(x)
device = x.device
x = x.squeeze(0).numpy()
x = self.extractor(
x, return_tensors=self.return_tensors, sampling_rate=self.sampling_rate
)
x = x["input_values"].squeeze(0).to(device)
# normalize
x = (x - x.mean()) / x.std()
return x
def train_lightning_ast(config: dict):
"""
work on integration between waveform dataset and environment. Should work for both HF and PTL.
"""
TARGET_CLASSES = config["dance_ids"]
DEVICE = config["device"]
SEED = config["seed"]
pl.seed_everything(SEED, workers=True)
feature_extractor = ASTExtractorWrapper()
dataset = get_datasets(config["datasets"], feature_extractor)
data = DanceDataModule(
dataset,
target_classes=TARGET_CLASSES,
**config["data_module"],
)
model = AST(TARGET_CLASSES).to(DEVICE)
label_weights = data.get_label_weights().to(DEVICE)
criterion = nn.CrossEntropyLoss(
label_weights
) # LabelWeightedBCELoss(label_weights)
if "checkpoint" in config:
train_env = TrainingEnvironment.load_from_checkpoint(
config["checkpoint"], criterion=criterion, model=model, config=config
)
else:
train_env = TrainingEnvironment(model, criterion, config)
callbacks = [
# cb.LearningRateFinder(update_attr=True),
cb.EarlyStopping("val/loss", patience=5),
cb.RichProgressBar(),
]
trainer = pl.Trainer(callbacks=callbacks, **config["trainer"])
trainer.fit(train_env, datamodule=data)
trainer.test(train_env, datamodule=data)
def train_huggingface_ast(config: dict):
TARGET_CLASSES = config["dance_ids"]
DEVICE = config["device"]
SEED = config["seed"]
OUTPUT_DIR = "models/weights/ast"
batch_size = config["data_module"]["batch_size"]
epochs = config["data_module"]["min_epochs"]
test_proportion = config["data_module"].get("test_proportion", 0.2)
pl.seed_everything(SEED, workers=True)
dataset = get_datasets(config["datasets"])
hf_dataset = HuggingFaceDatasetWrapper(dataset)
id2label, label2id = get_id_label_mapping(TARGET_CLASSES)
model_checkpoint = "MIT/ast-finetuned-audioset-10-10-0.4593"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_checkpoint)
preprocess_waveform = lambda wf: feature_extractor(
wf,
sampling_rate=train_ds.resample_frequency,
# padding="max_length",
# return_tensors="pt",
)
hf_dataset.append_to_pipeline(preprocess_waveform)
test_proportion = config["data_module"]["test_proportion"]
train_proporition = 1 - test_proportion
train_ds, test_ds = torch.utils.data.random_split(
hf_dataset, [train_proporition, test_proportion]
)
model = AutoModelForAudioClassification.from_pretrained(
model_checkpoint,
num_labels=len(TARGET_CLASSES),
label2id=label2id,
id2label=id2label,
ignore_mismatched_sizes=True,
).to(DEVICE)
training_args = TrainingArguments(
output_dir=OUTPUT_DIR,
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=5,
per_device_eval_batch_size=batch_size,
num_train_epochs=epochs,
warmup_ratio=0.1,
logging_steps=10,
load_best_model_at_end=True,
metric_for_best_model="accuracy",
push_to_hub=False,
use_mps_device=DEVICE == "mps",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset=test_ds,
tokenizer=feature_extractor,
compute_metrics=compute_hf_metrics,
)
trainer.train()
return model
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