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Running
on
Zero
File size: 3,358 Bytes
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import librosa
import numpy as np
import pandas as pd
import torch
from utmosv2.dataset._utils import (
extend_audio,
get_dataset_map,
load_audio,
select_random_start,
)
class MultiSpecDataset(torch.utils.data.Dataset):
def __init__(self, cfg, data: pd.DataFrame, phase: str, transform=None):
self.cfg = cfg
self.data = data
self.phase = phase
self.transform = transform
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
row = self.data.iloc[idx]
file = row["file_path"]
y = load_audio(self.cfg, file)
specs = []
length = int(self.cfg.dataset.spec_frames.frame_sec * self.cfg.sr)
y = extend_audio(self.cfg, y, length, type=self.cfg.dataset.spec_frames.extend)
for _ in range(self.cfg.dataset.spec_frames.num_frames):
y1 = select_random_start(y, length)
for spec_cfg in self.cfg.dataset.specs:
spec = _make_spctrogram(self.cfg, spec_cfg, y1)
if self.cfg.dataset.spec_frames.mixup_inner:
y2 = select_random_start(y, length)
spec2 = _make_spctrogram(self.cfg, spec_cfg, y2)
lmd = np.random.beta(
self.cfg.dataset.spec_frames.mixup_alpha,
self.cfg.dataset.spec_frames.mixup_alpha,
)
spec = lmd * spec + (1 - lmd) * spec2
spec = np.stack([spec, spec, spec], axis=0)
# spec = np.transpose(spec, (1, 2, 0))
spec = torch.tensor(spec, dtype=torch.float32)
phase = "train" if self.phase == "train" else "valid"
spec = self.transform[phase](spec)
specs.append(spec)
spec = torch.stack(specs).float()
target = row["mos"]
target = torch.tensor(target, dtype=torch.float32)
return spec, target
class MultiSpecExtDataset(MultiSpecDataset):
def __init__(self, cfg, data: pd.DataFrame, phase: str, transform=None):
super().__init__(cfg, data, phase, transform)
self.dataset_map = get_dataset_map(cfg)
def __getitem__(self, idx):
spec, target = super().__getitem__(idx)
d = np.zeros(len(self.dataset_map))
d[self.dataset_map[self.data.iloc[idx]["dataset"]]] = 1
d = torch.tensor(d, dtype=torch.float32)
return spec, d, target
def _make_spctrogram(cfg, spec_cfg, y: np.ndarray) -> np.ndarray:
if spec_cfg.mode == "melspec":
return _make_melspec(cfg, spec_cfg, y)
elif spec_cfg.mode == "stft":
return _make_stft(cfg, spec_cfg, y)
else:
raise NotImplementedError
def _make_melspec(cfg, spec_cfg, y: np.ndarray) -> np.ndarray:
spec = librosa.feature.melspectrogram(
y=y,
sr=cfg.sr,
n_fft=spec_cfg.n_fft,
hop_length=spec_cfg.hop_length,
n_mels=spec_cfg.n_mels,
)
spec = librosa.power_to_db(spec, ref=np.max)
if spec_cfg.norm is not None:
spec = (spec + spec_cfg.norm) / spec_cfg.norm
return spec
def _make_stft(cfg, spec_cfg, y: np.ndarray) -> np.ndarray:
spec = librosa.stft(y=y, n_fft=spec_cfg.n_fft, hop_length=spec_cfg.hop_length)
spec = np.abs(spec)
spec = librosa.amplitude_to_db(spec)
return spec
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