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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 pickle
from pathlib import Path
from typing import Optional
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
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}")
from datasets import load_dataset
import pretty_midi
import io
import tempfile
import os
ds = load_dataset(data_config.dataset_name, split="train", trust_remote_code=True)
all_sequences = []
errors = 0
logger.info(f"Tokenizing {len(ds)} MIDI files...")
for i, item in enumerate(ds):
try:
midi_bytes = item.get("midi") or item.get("audio") or item.get("file")
if midi_bytes is None:
# Try getting the bytes from any binary column
for key, val in item.items():
if isinstance(val, (bytes, dict)):
if isinstance(val, dict) and "bytes" in val:
midi_bytes = val["bytes"]
break
elif isinstance(val, bytes):
midi_bytes = val
break
if midi_bytes is None:
errors += 1
continue
if isinstance(midi_bytes, bytes):
# Write to temp file since pretty_midi needs a file path
with tempfile.NamedTemporaryFile(suffix=".mid", delete=False) as tmp:
tmp.write(midi_bytes)
tmp_path = tmp.name
try:
midi = pretty_midi.PrettyMIDI(tmp_path)
tokens = tokenizer.midi_to_tokens(midi, max_len=data_config.max_seq_len + 1)
if len(tokens) >= 20:
all_sequences.append(tokens)
finally:
os.unlink(tmp_path)
elif isinstance(midi_bytes, str):
# It's a file path
midi = pretty_midi.PrettyMIDI(midi_bytes)
tokens = tokenizer.midi_to_tokens(midi, max_len=data_config.max_seq_len + 1)
if len(tokens) >= 20:
all_sequences.append(tokens)
except Exception as e:
errors += 1
if errors <= 5:
logger.warning(f"Error processing item {i}: {e}")
if (i + 1) % 200 == 0:
logger.info(f" Processed {i+1}/{len(ds)}, 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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