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import os
import json
import numpy as np
from typing import Any, Tuple
import soundfile as sf
import torch
from pytorch_lightning import LightningDataModule
# from pytorch_lightning.core.mixins import HyperparametersMixin
from torch.utils.data import ConcatDataset, DataLoader, Dataset
from typing import Dict, Iterable, List, Iterator
from rich import print
from pytorch_lightning.utilities import rank_zero_only
@rank_zero_only
def print_(message: str):
print(message)
def normalize_tensor_wav(wav_tensor, eps=1e-8, std=None):
mean = wav_tensor.mean(-1, keepdim=True)
if std is None:
std = wav_tensor.std(-1, keepdim=True)
return (wav_tensor - mean) / (std + eps)
class MP3DDataset(Dataset):
def __init__(
self,
json_dir: str = "",
n_src: int = 2,
sample_rate: int = 8000,
segment: float = 4.0,
normalize_audio: bool = False,
) -> None:
super().__init__()
self.EPS = 1e-8
if json_dir == None:
raise ValueError("JSON DIR is None!")
if n_src not in [1, 2]:
raise ValueError("{} is not in [1, 2]".format(n_src))
self.json_dir = json_dir
self.sample_rate = sample_rate
self.normalize_audio = normalize_audio
if segment is None:
self.seg_len = None
self.fps_len = None
else:
self.seg_len = int(segment * sample_rate)
self.n_src = n_src
self.test = self.seg_len is None
mix_json = os.path.join(json_dir, "mix.json")
sources_json = [
os.path.join(json_dir, source + ".json") for source in ["s1", "s2"]
]
with open(mix_json, "r") as f:
mix_infos = json.load(f)
sources_infos = []
for src_json in sources_json:
with open(src_json, "r") as f:
sources_infos.append(json.load(f))
self.mix = []
self.sources = []
if self.n_src == 1:
orig_len = len(mix_infos) * 2
drop_utt, drop_len = 0, 0
if not self.test:
for i in range(len(mix_infos) - 1, -1, -1):
if mix_infos[i][1] < self.seg_len:
drop_utt = drop_utt + 1
drop_len = drop_len + mix_infos[i][1]
del mix_infos[i]
for src_inf in sources_infos:
del src_inf[i]
else:
for src_inf in sources_infos:
self.mix.append(mix_infos[i])
self.sources.append(src_inf[i])
else:
for i in range(len(mix_infos)):
for src_inf in sources_infos:
self.mix.append(mix_infos[i])
self.sources.append(src_inf[i])
print_(
"Drop {} utts({:.2f} h) from {} (shorter than {} samples)".format(
drop_utt, drop_len / sample_rate / 3600, orig_len, self.seg_len
)
)
self.length = len(self.mix)
elif self.n_src == 2:
orig_len = len(mix_infos)
drop_utt, drop_len = 0, 0
if not self.test:
for i in range(len(mix_infos) - 1, -1, -1): # Go backward
if mix_infos[i][1] < self.seg_len:
drop_utt = drop_utt + 1
drop_len = drop_len + mix_infos[i][1]
del mix_infos[i]
for src_inf in sources_infos:
del src_inf[i]
print_(
"Drop {} utts({:.2f} h) from {} (shorter than {} samples)".format(
drop_utt, drop_len / sample_rate / 36000, orig_len, self.seg_len
)
)
self.mix = mix_infos
self.sources = sources_infos
self.length = len(self.mix)
def __len__(self):
return self.length
def preprocess_audio_only(self, idx: int):
if self.n_src == 1:
if self.mix[idx][1] == self.seg_len or self.test:
rand_start = 0
else:
rand_start = np.random.randint(0, self.mix[idx][1] - self.seg_len)
if self.test:
stop = None
else:
stop = rand_start + self.seg_len
# Load mixture
x, _ = sf.read(
self.mix[idx][0], start=rand_start, stop=stop, dtype="float32"
)
# Load sources
s, _ = sf.read(
self.sources[idx][0], start=rand_start, stop=stop, dtype="float32"
)
# torch from numpy
target = torch.from_numpy(s)
mixture = torch.from_numpy(x)
if self.normalize_audio:
m_std = mixture.std(-1, keepdim=True)
mixture = normalize_tensor_wav(mixture, eps=self.EPS, std=m_std)
target = normalize_tensor_wav(target, eps=self.EPS, std=m_std)
# return mixture, target.unsqueeze(0), self.mix[idx][0].split("/")[-1]
return mixture, target.unsqueeze(0), self.mix[idx][0]
# import pdb; pdb.set_trace()
if self.n_src == 2:
if self.mix[idx][1] == self.seg_len or self.test:
rand_start = 0
else:
rand_start = np.random.randint(0, self.mix[idx][1] - self.seg_len)
if self.test:
stop = None
else:
stop = rand_start + self.seg_len
# Load mixture
x, _ = sf.read(
self.mix[idx][0], start=rand_start, stop=stop, dtype="float32"
)
# Load sources
source_arrays = []
for src in self.sources:
s, _ = sf.read(
src[idx][0], start=rand_start, stop=stop, dtype="float32"
)
source_arrays.append(s)
sources = torch.from_numpy(np.vstack(source_arrays))
mixture = torch.from_numpy(x)
if self.normalize_audio:
m_std = mixture.std(-1, keepdim=True)
mixture = normalize_tensor_wav(mixture, eps=self.EPS, std=m_std)
sources = normalize_tensor_wav(sources, eps=self.EPS, std=m_std)
# return mixture, sources, self.mix[idx][0].split("/")[-1]
return mixture, sources, self.mix[idx][0]
def __getitem__(self, index: int):
return self.preprocess_audio_only(index)
class EchoSetDataModule(object):
def __init__(
self,
train_dir: str,
valid_dir: str,
test_dir: str,
n_src: int = 2,
sample_rate: int = 8000,
segment: float = 4.0,
normalize_audio: bool = False,
batch_size: int = 64,
num_workers: int = 0,
pin_memory: bool = False,
persistent_workers: bool = False,
) -> None:
super().__init__()
if train_dir == None or valid_dir == None or test_dir == None:
raise ValueError("JSON DIR is None!")
if n_src not in [1, 2]:
raise ValueError("{} is not in [1, 2]".format(n_src))
# this line allows to access init params with 'self.hparams' attribute
self.train_dir = train_dir
self.valid_dir = valid_dir
self.test_dir = test_dir
self.n_src = n_src
self.sample_rate = sample_rate
self.segment = segment
self.normalize_audio = normalize_audio
self.batch_size = batch_size
self.num_workers = num_workers
self.pin_memory = pin_memory
self.persistent_workers = persistent_workers
self.data_train: Dataset = None
self.data_val: Dataset = None
self.data_test: Dataset = None
def setup(self) -> None:
self.data_train = MP3DDataset(
json_dir=self.train_dir,
n_src=self.n_src,
sample_rate=self.sample_rate,
segment=self.segment,
normalize_audio=self.normalize_audio,
)
self.data_val = MP3DDataset(
json_dir=self.valid_dir,
n_src=self.n_src,
sample_rate=self.sample_rate,
segment=None,
normalize_audio=self.normalize_audio,
)
self.data_test = MP3DDataset(
json_dir=self.test_dir,
n_src=self.n_src,
sample_rate=self.sample_rate,
segment=None,
normalize_audio=self.normalize_audio,
)
def train_dataloader(self) -> DataLoader:
return DataLoader(
dataset=self.data_train,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
persistent_workers=self.persistent_workers,
pin_memory=self.pin_memory,
drop_last=True,
)
def val_dataloader(self) -> DataLoader:
return DataLoader(
dataset=self.data_val,
shuffle=False,
batch_size=self.batch_size,
num_workers=self.num_workers,
persistent_workers=self.persistent_workers,
pin_memory=self.pin_memory,
drop_last=False,
)
def test_dataloader(self) -> DataLoader:
return DataLoader(
dataset=self.data_test,
shuffle=False,
batch_size=self.batch_size,
num_workers=self.num_workers,
persistent_workers=self.persistent_workers,
pin_memory=self.pin_memory,
drop_last=False,
)
@property
def make_loader(self):
return self.train_dataloader(), self.val_dataloader(), self.test_dataloader()
@property
def make_sets(self):
return self.data_train, self.data_val, self.data_test
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