dataset.py
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import json
import random
from pathlib import Path
from typing import Dict, List, Optional
import librosa
import numpy as np
import torch
from torch.utils.data import Dataset
class ACRDataset(Dataset):
def __init__(
self,
data_dir: str,
split: str = "train",
sr: int = 16000,
n_mels: int = 80,
n_fft: int = 512,
hop_length: int = 160,
segment_dur: float = 5.0,
augment: bool = True,
n_crops_per_song: int = 4,
song_to_idx: Optional[Dict[str, int]] = None,
references_only: bool = False,
):
self.sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.hop_length = hop_length
self.segment_len = int(segment_dur * sr)
self.augment = augment
self.n_crops = n_crops_per_song
self.data_dir = Path(data_dir)
meta_path = self.data_dir / f"{split}.json"
with open(meta_path) as f:
self.metadata = json.load(f)
self.samples = []
for item in self.metadata:
if references_only and item.get("type") != "reference":
continue
song_path = self.data_dir / item["audio_path"]
if song_path.exists():
self.samples.append(item)
self.song_ids = sorted(set(s["song_id"] for s in self.samples))
self.song_to_idx = song_to_idx or {sid: i for i, sid in enumerate(self.song_ids)}
def __len__(self):
return len(self.samples) * self.n_crops
def _load_segment(self, path: str, offset: float, duration: float) -> np.ndarray:
y, _ = librosa.load(path, sr=self.sr, mono=True, offset=offset, duration=duration)
if len(y) < self.segment_len:
y = np.pad(y, (0, self.segment_len - len(y)))
else:
y = y[: self.segment_len]
return y
def _to_mel(self, y: np.ndarray) -> np.ndarray:
mel = librosa.feature.melspectrogram(
y=y,
sr=self.sr,
n_mels=self.n_mels,
n_fft=self.n_fft,
hop_length=self.hop_length,
)
return librosa.power_to_db(mel, ref=np.max)
def __getitem__(self, idx):
sample = self.samples[idx // self.n_crops]
duration = sample["duration"]
max_offset = max(0, duration - 5.0)
offset = random.uniform(0, max_offset) if max_offset > 0 else 0
audio_path = self.data_dir / sample["audio_path"]
y = self._load_segment(str(audio_path), offset, 5.0)
if self.augment and sample.get("type") != "reference":
from src.utils.augment import AugmentPipeline
aug = AugmentPipeline(self.sr)
y = aug(y)
mel = self._to_mel(y)
mel_tensor = torch.FloatTensor(mel)
song_id = sample["song_id"]
class_id = self.song_to_idx[song_id]
return {
"mel": mel_tensor,
"song_id": torch.tensor(class_id, dtype=torch.long),
"song_name": song_id,
"type": sample.get("type", "unknown"),
}
class ACRTestDataset(Dataset):
def __init__(
self,
data_dir: str,
split: str = "test",
sr: int = 16000,
n_mels: int = 80,
n_fft: int = 512,
hop_length: int = 160,
song_to_idx: Optional[Dict[str, int]] = None,
):
self.sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.hop_length = hop_length
self.data_dir = Path(data_dir)
meta_path = self.data_dir / f"{split}.json"
with open(meta_path) as f:
self.metadata = json.load(f)
self.samples = []
for item in self.metadata:
p = self.data_dir / item["audio_path"]
if p.exists():
self.samples.append(item)
self.song_ids = sorted(set(s["song_id"] for s in self.samples))
self.song_to_idx = song_to_idx or {sid: i for i, sid in enumerate(self.song_ids)}
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
sample = self.samples[idx]
audio_path = self.data_dir / sample["audio_path"]
y, _ = librosa.load(str(audio_path), sr=self.sr, mono=True, offset=0, duration=min(sample["duration"], 5.0))
seg_len = 5 * self.sr
if len(y) < seg_len:
y = np.pad(y, (0, seg_len - len(y)))
else:
y = y[:seg_len]
mel = librosa.power_to_db(
librosa.feature.melspectrogram(
y=y,
sr=self.sr,
n_mels=self.n_mels,
n_fft=self.n_fft,
hop_length=self.hop_length,
),
ref=np.max,
)
class_id = self.song_to_idx[sample["song_id"]]
return {
"mel": torch.FloatTensor(mel),
"song_id": torch.tensor(class_id, dtype=torch.long),
"song_name": sample["song_id"],
"type": sample.get("type", "unknown"),
}
class SongPairDataset(Dataset):
def __init__(
self,
data_dir: str,
split: str = "train",
sr: int = 16000,
n_mels: int = 80,
n_fft: int = 512,
hop_length: int = 160,
segment_dur: float = 5.0,
augment: bool = True,
):
self.sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.hop_length = hop_length
self.segment_len = int(segment_dur * sr)
self.augment = augment
self.data_dir = Path(data_dir)
with open(self.data_dir / f"{split}.json") as f:
metadata = json.load(f)
self.by_song: Dict[str, List[Dict]] = {}
for item in metadata:
if item.get("type") == "reference":
continue
p = self.data_dir / item["audio_path"]
if p.exists():
self.by_song.setdefault(item["song_id"], []).append(item)
self.song_ids = sorted(self.by_song)
self.sample_song_ids = []
for sid, items in self.by_song.items():
item_types = {x.get("type") for x in items}
if "confused" in item_types:
weight = 5
elif "humming_like" in item_types:
weight = 3
else:
weight = 1
self.sample_song_ids.extend([sid] * weight)
self.song_to_idx = {sid: i for i, sid in enumerate(self.song_ids)}
def __len__(self):
return len(self.sample_song_ids)
def _load_clip(self, sample: Dict) -> np.ndarray:
path = self.data_dir / sample["audio_path"]
y, _ = librosa.load(str(path), sr=self.sr, mono=True, duration=5.0)
if len(y) < self.segment_len:
y = np.pad(y, (0, self.segment_len - len(y)))
else:
y = y[: self.segment_len]
return y
def _to_mel(self, y: np.ndarray) -> torch.Tensor:
mel = librosa.feature.melspectrogram(
y=y,
sr=self.sr,
n_mels=self.n_mels,
n_fft=self.n_fft,
hop_length=self.hop_length,
)
mel = librosa.power_to_db(mel, ref=np.max)
return torch.FloatTensor(mel)
def __getitem__(self, idx):
song_id = self.sample_song_ids[idx]
choices = self.by_song[song_id]
if len(choices) == 1:
a = b = choices[0]
else:
a, b = random.sample(choices, 2)
type_to_weight = {
"confused": 4.0,
"humming_like": 2.5,
"augmented": 1.4,
}
pair_weights = [
type_to_weight.get(a.get("type", "unknown"), 1.0),
type_to_weight.get(b.get("type", "unknown"), 1.0),
]
wavs = []
for sample in (a, b):
y = self._load_clip(sample)
if self.augment:
from src.utils.augment import AugmentPipeline
y = AugmentPipeline(self.sr, aggressive=sample.get("type") in {"confused", "humming_like"})(y)
wavs.append(self._to_mel(y))
max_t = max(w.shape[1] for w in wavs)
wavs = [torch.nn.functional.pad(w, (0, max_t - w.shape[1])) if w.shape[1] < max_t else w for w in wavs]
label = self.song_to_idx[song_id]
return {
"mel": torch.stack(wavs, dim=0),
"song_id": torch.tensor([label, label], dtype=torch.long),
"song_name": song_id,
"hard_weight": torch.tensor(pair_weights, dtype=torch.float32),
}