import pytorch_lightning as pl
from sklearn.base import ClassifierMixin, BaseEstimator
import pandas as pd
from torch import nn
import torch
from typing import Iterator
import numpy as np
import json
from torch.utils.data import random_split
from tqdm import tqdm
import librosa
from joblib import dump, load
from os import path
import os
from preprocessing.dataset import get_music4dance_examples
DANCE_INFO_FILE = "data/dance_info.csv"
dance_info_df = pd.read_csv(
DANCE_INFO_FILE,
converters={"tempoRange": lambda s: json.loads(s.replace("'", '"'))},
)
class DanceTreeClassifier(BaseEstimator, ClassifierMixin):
"""
Trains a series of binary classifiers to classify each dance when a song falls into its bpm range.
Features:
- Spectrogram
- BPM
"""
def __init__(self, device="cpu", lr=1e-4, verbose=True) -> None:
self.device = device
self.verbose = verbose
self.lr = lr
self.classifiers = {}
self.optimizers = {}
self.criterion = nn.BCELoss()
def get_valid_dances_from_bpm(self, bpm: float) -> list[str]:
mask = dance_info_df["tempoRange"].apply(
lambda interval: interval["min"] <= bpm <= interval["max"]
)
return list(dance_info_df["id"][mask])
def fit(self, x, y):
"""
x: (specs, bpms). The first element is the spectrogram, second element is the bpm. spec shape should be (channel, freq_bins, sr * time)
y: (batch_size, n_classes)
"""
epoch_loss = 0
pred_count = 0
data_loader = zip(x, y)
if self.verbose:
data_loader = tqdm(data_loader, total=len(y))
for (spec, bpm), label in data_loader:
# find all models that are in the bpm range
matching_dances = self.get_valid_dances_from_bpm(bpm)
spec = torch.from_numpy(spec).to(self.device)
for dance in matching_dances:
if dance not in self.classifiers or dance not in self.optimizers:
classifier = DanceCNN().to(self.device)
self.classifiers[dance] = classifier
self.optimizers[dance] = torch.optim.Adam(
classifier.parameters(), lr=self.lr
)
models = [
(dance, model, self.optimizers[dance])
for dance, model in self.classifiers.items()
if dance in matching_dances
]
for model_i, (dance, model, opt) in enumerate(models, start=1):
opt.zero_grad()
output = model(spec)
target = torch.tensor([float(dance == label)], device=self.device)
loss = self.criterion(output, target)
epoch_loss += loss.item()
pred_count += 1
loss.backward()
if self.verbose:
data_loader.set_description(
f"model: {model_i}/{len(models)}, loss: {loss.item()}"
)
opt.step()
def predict(self, x) -> list[str]:
results = []
for spec, bpm in zip(*x):
matching_dances = self.get_valid_dances_from_bpm(bpm)
dance_i = torch.tensor(
[self.classifiers[dance](spec) for dance in matching_dances]
).argmax()
results.append(matching_dances[dance_i])
return results
def save(self, folder: str):
# Create a folder
classifier_path = path.join(folder, "classifier")
os.makedirs(classifier_path, exist_ok=True)
# Swap out model reference
classifiers = self.classifiers
optimizers = self.optimizers
criterion = self.criterion
self.classifiers = None
self.optimizers = None
self.criterion = None
# Save the Pth models
for dance, classifier in classifiers.items():
torch.save(
classifier.state_dict(), path.join(classifier_path, dance + ".pth")
)
# Save the Sklearn model
dump(path.join(folder, "sklearn.joblib"))
# Reload values
self.classifiers = classifiers
self.optimizers = optimizers
self.criterion = criterion
@staticmethod
def from_config(folder: str, device="cpu") -> "DanceTreeClassifier":
# load in weights
model_paths = (
p for p in os.listdir(path.join(folder, "classifier")) if p.endswith("pth")
)
classifiers = {}
for model_path in model_paths:
dance = model_path.split(".")[0]
model = DanceCNN().to(device)
model.load_state_dict(
torch.load(path.join(folder, "classifier", model_path))
)
classifiers[dance] = model
wrapper = load(path.join(folder, "sklearn.joblib"))
wrapper.classifiers = classifiers
return wrapper
class DanceCNN(nn.Module):
def __init__(self, sr=16000, freq_bins=20, duration=6, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
kernel_size = (3, 9)
self.cnn = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=kernel_size),
nn.ReLU(),
nn.MaxPool2d((2, 10)),
nn.Conv2d(16, 32, kernel_size=kernel_size),
nn.ReLU(),
nn.MaxPool2d((2, 10)),
nn.Conv2d(32, 32, kernel_size=kernel_size),
nn.ReLU(),
nn.MaxPool2d((2, 10)),
nn.Conv2d(32, 16, kernel_size=kernel_size),
nn.ReLU(),
nn.MaxPool2d((2, 10)),
)
embedding_dimension = 16 * 6 * 8
self.classifier = nn.Sequential(
nn.Linear(embedding_dimension, 200),
nn.ReLU(),
nn.Linear(200, 1),
nn.Sigmoid(),
)
def forward(self, x):
x = self.cnn(x)
x = x.flatten() if len(x.shape) == 3 else x.flatten(1)
return self.classifier(x)
def features_from_path(
paths: list[str], audio_window_duration=6, audio_duration=30, resample_freq=16000
) -> Iterator[tuple[np.array, float]]:
"""
Loads audio and bpm from an audio path.
"""
for path in paths:
waveform, sr = librosa.load(path, mono=True, sr=resample_freq)
num_frames = audio_window_duration * sr
tempo, _ = librosa.beat.beat_track(y=waveform, sr=sr)
spec = librosa.feature.melspectrogram(y=waveform, sr=sr)
spec_normalized = (spec - spec.mean()) / spec.std()
spec_padded = librosa.util.fix_length(
spec_normalized, size=sr * audio_duration, axis=1
)
batched_spec = np.expand_dims(spec_padded, axis=0)
for i in range(audio_duration // audio_window_duration):
spec_window = batched_spec[:, :, i * num_frames : (i + 1) * num_frames]
yield (spec_window, tempo)
def train_decision_tree(config: dict):
TARGET_CLASSES = config["global"]["dance_ids"]
DEVICE = config["global"]["device"]
SEED = config["global"]["seed"]
SEED = config["global"]["seed"]
EPOCHS = config["trainer"]["min_epochs"]
song_data_path = config["data_module"]["song_data_path"]
song_audio_path = config["data_module"]["song_audio_path"]
pl.seed_everything(SEED, workers=True)
df = pd.read_csv(song_data_path)
x, y = get_music4dance_examples(
df, song_audio_path, class_list=TARGET_CLASSES, multi_label=True
)
# Convert y back to string classes
y = np.array(TARGET_CLASSES)[y.argmax(-1)]
train_i, test_i = random_split(
np.arange(len(x)), [0.1, 0.9]
) # Temporary to test efficacy
train_paths, train_y = x[train_i], y[train_i]
model = DanceTreeClassifier(device=DEVICE)
for epoch in tqdm(range(1, EPOCHS + 1)):
# Shuffle the data
i = np.arange(len(train_paths))
np.random.shuffle(i)
train_paths = train_paths[i]
train_y = train_y[i]
train_x = features_from_path(train_paths)
model.fit(train_x, train_y)
# evaluate the model
preds = model.predict(x[test_i])
accuracy = (preds == y[test_i]).mean()
print(f"{accuracy=}")
model.save("models/weights/decision_tree")