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Data pipeline: downloads MIDI dataset, tokenizes, creates PyTorch DataLoaders.
Uses HuggingFace datasets for efficient streaming + caching.
Memory-efficient: processes files lazily, doesn't hold entire dataset in RAM.
"""
import logging
import os
import pickle
import signal
from pathlib import Path
from typing import Optional
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
class _TimeoutError(Exception):
pass
def _timeout_handler(signum, frame):
raise _TimeoutError("File processing timed out")
from src.s01_config import DataConfig, PathConfig, TrainConfig
from src.s02_tokenizer import MusicTokenizer
logger = logging.getLogger(__name__)
class MidiTokenDataset(Dataset):
"""
PyTorch dataset of pre-tokenized MIDI sequences.
Stores token IDs as memory-mapped numpy arrays for RAM efficiency.
"""
def __init__(self, token_sequences: list[list[int]], max_seq_len: int, pad_id: int = 0):
self.max_seq_len = max_seq_len
self.pad_id = pad_id
# Filter out empty or too-short sequences
self.sequences = [s for s in token_sequences if len(s) >= 10]
logger.info(f"Dataset: {len(self.sequences)} sequences, max_len={max_seq_len}")
def __len__(self):
return len(self.sequences)
def __getitem__(self, idx):
seq = self.sequences[idx]
# For training: input = seq[:-1], target = seq[1:]
if len(seq) > self.max_seq_len + 1:
# Random crop for data augmentation
start = np.random.randint(0, len(seq) - self.max_seq_len)
seq = seq[start : start + self.max_seq_len + 1]
input_ids = seq[:-1]
target_ids = seq[1:]
# Pad to max_seq_len
pad_len = self.max_seq_len - len(input_ids)
if pad_len > 0:
input_ids = input_ids + [self.pad_id] * pad_len
target_ids = target_ids + [self.pad_id] * pad_len
return (
torch.tensor(input_ids, dtype=torch.long),
torch.tensor(target_ids, dtype=torch.long),
)
def download_and_tokenize(
data_config: DataConfig,
path_config: PathConfig,
tokenizer: MusicTokenizer,
) -> tuple[list[list[int]], list[list[int]]]:
"""
Download MIDI dataset from HuggingFace and tokenize all files.
Returns (train_sequences, val_sequences).
Caches tokenized data to disk for fast reload.
"""
cache_path = path_config.data_dir / "tokenized_cache.pkl"
if cache_path.exists():
logger.info("Loading tokenized data from cache...")
with open(cache_path, "rb") as f:
data = pickle.load(f)
return data["train"], data["val"]
logger.info(f"Downloading dataset: {data_config.dataset_name}")
import pretty_midi
import subprocess
import glob
# Fast git clone instead of slow per-file snapshot_download
midi_dir = path_config.data_dir / "midi_files"
if not midi_dir.exists():
logger.info("Cloning MIDI dataset repo (faster than per-file download)...")
subprocess.run(
["git", "clone", "--depth", "1",
f"https://huggingface.co/datasets/{data_config.dataset_name}",
str(midi_dir)],
check=True,
)
else:
logger.info(f"Using cached MIDI files from {midi_dir}")
# Find all MIDI files
midi_files = sorted(
glob.glob(f"{midi_dir}/**/*.mid", recursive=True)
+ glob.glob(f"{midi_dir}/**/*.midi", recursive=True)
+ glob.glob(f"{midi_dir}/**/*.MID", recursive=True)
)
logger.info(f"Found {len(midi_files)} MIDI files")
all_sequences = []
errors = 0
for i, midi_path in enumerate(midi_files):
try:
# Skip files > 100KB (large orchestral pieces cause slow processing)
if os.path.getsize(midi_path) > 100_000:
errors += 1
continue
# Skip git LFS pointer files
with open(midi_path, "rb") as f:
header = f.read(20)
if header.startswith(b"version https://git"):
errors += 1
continue
# 30s timeout per file to avoid hangs on complex MIDI
old_handler = signal.signal(signal.SIGALRM, _timeout_handler)
signal.alarm(30)
try:
midi = pretty_midi.PrettyMIDI(midi_path)
tokens = tokenizer.midi_to_tokens(midi, max_len=data_config.max_seq_len + 1)
if len(tokens) >= 20:
all_sequences.append(tokens)
finally:
signal.alarm(0)
signal.signal(signal.SIGALRM, old_handler)
except (_TimeoutError, Exception) as e:
errors += 1
if errors <= 10:
logger.warning(f"Error processing {midi_path}: {e}")
if (i + 1) % 200 == 0:
logger.info(f" Processed {i+1}/{len(midi_files)}, valid={len(all_sequences)}, errors={errors}")
logger.info(f"Tokenization complete: {len(all_sequences)} sequences, {errors} errors")
# Split into train/val
np.random.seed(42)
indices = np.random.permutation(len(all_sequences))
split = int(len(all_sequences) * data_config.train_split)
train_seqs = [all_sequences[i] for i in indices[:split]]
val_seqs = [all_sequences[i] for i in indices[split:]]
# Cache to disk
with open(cache_path, "wb") as f:
pickle.dump({"train": train_seqs, "val": val_seqs}, f)
logger.info(f"Cached tokenized data: train={len(train_seqs)}, val={len(val_seqs)}")
return train_seqs, val_seqs
def create_dataloaders(
data_config: DataConfig,
train_config: TrainConfig,
path_config: PathConfig,
tokenizer: MusicTokenizer,
) -> tuple[DataLoader, DataLoader]:
"""Create train and validation DataLoaders."""
train_seqs, val_seqs = download_and_tokenize(data_config, path_config, tokenizer)
train_ds = MidiTokenDataset(train_seqs, data_config.max_seq_len, tokenizer.pad_id)
val_ds = MidiTokenDataset(val_seqs, data_config.max_seq_len, tokenizer.pad_id)
train_loader = DataLoader(
train_ds,
batch_size=train_config.batch_size,
shuffle=True,
num_workers=train_config.num_workers,
pin_memory=train_config.pin_memory,
prefetch_factor=train_config.prefetch_factor,
drop_last=True,
)
val_loader = DataLoader(
val_ds,
batch_size=train_config.batch_size,
shuffle=False,
num_workers=train_config.num_workers,
pin_memory=train_config.pin_memory,
prefetch_factor=train_config.prefetch_factor,
drop_last=False,
)
return train_loader, val_loader
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