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import os
import h5py
import numpy as np
from typing import Any, Tuple
import torch
import random
from pytorch_lightning import LightningDataModule
import torchaudio
from torchaudio.functional import apply_codec
from torch.utils.data import DataLoader, Dataset
from typing import Any, Dict, Optional, Tuple
def compute_mch_rms_dB(mch_wav, fs=16000, energy_thresh=-50):
"""Return the wav RMS calculated only in the active portions"""
mean_square = max(1e-20, torch.mean(mch_wav ** 2))
return 10 * np.log10(mean_square)
def match2(x, d):
assert x.dim()==2, x.shape
assert d.dim()==2, d.shape
minlen = min(x.shape[-1], d.shape[-1])
x, d = x[:,0:minlen], d[:,0:minlen]
Fx = torch.fft.rfft(x, dim=-1)
Fd = torch.fft.rfft(d, dim=-1)
Phi = Fd*Fx.conj()
Phi = Phi / (Phi.abs() + 1e-3)
Phi[:,0] = 0
tmp = torch.fft.irfft(Phi, dim=-1)
tau = torch.argmax(tmp.abs(),dim=-1).tolist()
return tau
def codec_simu(wav, sr=16000, options={'bitrate':'random','compression':'random', 'complexity':'random', 'vbr':'random'}):
if options['bitrate'] == 'random':
options['bitrate'] = random.choice([24000, 32000, 48000, 64000, 96000, 128000])
compression = int(options['bitrate']//1000)
param = {'format': "mp3", "compression": compression}
wav_encdec = apply_codec(wav, sr, **param)
if wav_encdec.shape[-1] >= wav.shape[-1]:
wav_encdec = wav_encdec[...,:wav.shape[-1]]
else:
wav_encdec = torch.cat([wav_encdec, wav[..., wav_encdec.shape[-1]:]], -1)
tau = match2(wav, wav_encdec)
wav_encdec = torch.roll(wav_encdec, -tau[0], -1)
return wav_encdec
def get_wav_files(root_dir):
wav_files = []
for dirpath, dirnames, filenames in os.walk(root_dir):
for filename in filenames:
if filename.endswith('.wav'):
if "musdb18hq" in dirpath and "mixture" not in filename:
wav_files.append(os.path.join(dirpath, filename))
elif "moisesdb" in dirpath:
wav_files.append(os.path.join(dirpath, filename))
return wav_files
class MusdbMoisesdbDataset(Dataset):
def __init__(
self,
data_dir: str,
codec_type: str,
codec_options: dict,
sr: int = 16000,
segments: int = 10,
num_stems: int = 4,
snr_range: Tuple[int, int] = (-10, 10),
num_samples: int = 1000,
) -> None:
self.data_dir = data_dir
self.codec_type = codec_type
self.codec_options = codec_options
self.segments = int(segments * sr)
self.sr = sr
self.num_stems = num_stems
self.snr_range = snr_range
self.num_samples = num_samples
self.instruments = [
"bass",
"bowed_strings",
"drums",
"guitar",
"other",
"other_keys",
"other_plucked",
"percussion",
"piano",
"vocals",
"wind"
]
def __len__(self) -> int:
return self.num_samples
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
if random.random() > 0.5:
select_stems = random.randint(1, self.num_stems)
select_stems = random.choices(self.instruments, k=select_stems)
ori_wav = []
for stem in select_stems:
h5path = random.choice(os.listdir(os.path.join(self.data_dir, stem)))
datas = h5py.File(os.path.join(self.data_dir, stem, h5path), 'r')['data']
random_index = random.randint(0, datas.shape[0]-1)
music_wav = torch.FloatTensor(datas[random_index])
start = random.randint(0, music_wav.shape[-1] - self.segments)
music_wav = music_wav[:, start:start+self.segments]
rescale_snr = random.randint(self.snr_range[0], self.snr_range[1])
music_wav = music_wav * np.sqrt(10**(rescale_snr/10))
ori_wav.append(music_wav)
ori_wav = torch.stack(ori_wav).sum(0)
else:
h5path = random.choice(os.listdir(os.path.join(self.data_dir, "mixture")))
datas = h5py.File(os.path.join(self.data_dir, "mixture", h5path), 'r')['data']
random_index = random.randint(0, datas.shape[0]-1)
music_wav = torch.FloatTensor(datas[random_index])
start = random.randint(0, music_wav.shape[-1] - self.segments)
ori_wav = music_wav[:, start:start+self.segments]
codec_wav = codec_simu(ori_wav, sr=self.sr, options=self.codec_options)
max_scale = max(ori_wav.abs().max(), codec_wav.abs().max())
if max_scale > 0:
ori_wav = ori_wav / max_scale
codec_wav = codec_wav / max_scale
return ori_wav, codec_wav
class MusdbMoisesdbEval(Dataset):
def __init__(
self,
data_dir: str
) -> None:
self.data_path = os.listdir(data_dir)
self.data_path = [os.path.join(data_dir, i) for i in self.data_path]
def __len__(self) -> int:
return len(self.data_path)
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
ori_wav = torchaudio.load(self.data_path[idx]+"/ori_wav.wav")[0]
codec_wav = torchaudio.load(self.data_path[idx]+"/codec_wav.wav")[0]
return ori_wav, codec_wav, self.data_path[idx]
class MusdbMoisesdbDataModule(LightningDataModule):
def __init__(
self,
train_dir: str,
eval_dir: str,
codec_type: str,
codec_options: dict,
sr: int = 16000,
segments: int = 10,
num_stems: int = 4,
snr_range: Tuple[int, int] = (-10, 10),
num_samples: int = 1000,
batch_size: int = 32,
num_workers: int = 4,
) -> None:
super().__init__()
self.save_hyperparameters(logger=False)
self.data_train: Optional[Dataset] = None
self.data_val: Optional[Dataset] = None
def setup(self, stage: Optional[str] = None) -> None:
"""Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
This method is called by Lightning before `trainer.fit()`, `trainer.validate()`, `trainer.test()`, and
`trainer.predict()`, so be careful not to execute things like random split twice! Also, it is called after
`self.prepare_data()` and there is a barrier in between which ensures that all the processes proceed to
`self.setup()` once the data is prepared and available for use.
:param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``.
"""
# load and split datasets only if not loaded already
if not self.data_train and not self.data_val:
self.data_train = MusdbMoisesdbDataset(
data_dir=self.hparams.train_dir,
codec_type=self.hparams.codec_type,
codec_options=self.hparams.codec_options,
sr=self.hparams.sr,
segments=self.hparams.segments,
num_stems=self.hparams.num_stems,
snr_range=self.hparams.snr_range,
num_samples=self.hparams.num_samples,
)
self.data_val = MusdbMoisesdbEval(
data_dir=self.hparams.eval_dir
)
def train_dataloader(self) -> DataLoader:
return DataLoader(
self.data_train,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
shuffle=True,
pin_memory=True,
)
def val_dataloader(self) -> DataLoader:
return DataLoader(
self.data_val,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
shuffle=False,
pin_memory=True,
)
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