370 lines
11 KiB
Python
370 lines
11 KiB
Python
import math
|
|
import multiprocessing
|
|
import os
|
|
from pathlib import Path
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
import pytorch_lightning as pl
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch.utils.data import DataLoader, Dataset
|
|
from torch_audiomentations import Compose
|
|
|
|
from enhancer.data.fileprocessor import Fileprocessor
|
|
from enhancer.utils import check_files
|
|
from enhancer.utils.config import Files
|
|
from enhancer.utils.io import Audio
|
|
from enhancer.utils.random import create_unique_rng
|
|
|
|
LARGE_NUM = 2147483647
|
|
|
|
|
|
class TrainDataset(Dataset):
|
|
def __init__(self, dataset):
|
|
self.dataset = dataset
|
|
|
|
def __getitem__(self, idx):
|
|
return self.dataset.train__getitem__(idx)
|
|
|
|
def __len__(self):
|
|
return self.dataset.train__len__()
|
|
|
|
|
|
class ValidDataset(Dataset):
|
|
def __init__(self, dataset):
|
|
self.dataset = dataset
|
|
|
|
def __getitem__(self, idx):
|
|
return self.dataset.val__getitem__(idx)
|
|
|
|
def __len__(self):
|
|
return self.dataset.val__len__()
|
|
|
|
|
|
class TestDataset(Dataset):
|
|
def __init__(self, dataset):
|
|
self.dataset = dataset
|
|
|
|
def __getitem__(self, idx):
|
|
return self.dataset.test__getitem__(idx)
|
|
|
|
def __len__(self):
|
|
return self.dataset.test__len__()
|
|
|
|
|
|
class TaskDataset(pl.LightningDataModule):
|
|
def __init__(
|
|
self,
|
|
name: str,
|
|
root_dir: str,
|
|
files: Files,
|
|
valid_minutes: float = 0.20,
|
|
duration: float = 1.0,
|
|
stride=None,
|
|
sampling_rate: int = 48000,
|
|
matching_function=None,
|
|
batch_size=32,
|
|
num_workers: Optional[int] = None,
|
|
augmentations: Optional[Compose] = None,
|
|
):
|
|
super().__init__()
|
|
|
|
self.name = name
|
|
self.files, self.root_dir = check_files(root_dir, files)
|
|
self.duration = duration
|
|
self.stride = stride or duration
|
|
self.sampling_rate = sampling_rate
|
|
self.batch_size = batch_size
|
|
self.matching_function = matching_function
|
|
self._validation = []
|
|
if num_workers is None:
|
|
num_workers = multiprocessing.cpu_count() // 2
|
|
self.num_workers = num_workers
|
|
if valid_minutes > 0.0:
|
|
self.valid_minutes = valid_minutes
|
|
else:
|
|
raise ValueError("valid_minutes must be greater than 0")
|
|
|
|
self.augmentations = augmentations
|
|
|
|
def setup(self, stage: Optional[str] = None):
|
|
"""
|
|
prepare train/validation/test data splits
|
|
"""
|
|
|
|
if stage in ("fit", None):
|
|
|
|
train_clean = os.path.join(self.root_dir, self.files.train_clean)
|
|
train_noisy = os.path.join(self.root_dir, self.files.train_noisy)
|
|
fp = Fileprocessor.from_name(
|
|
self.name, train_clean, train_noisy, self.matching_function
|
|
)
|
|
train_data = fp.prepare_matching_dict()
|
|
train_data, self.val_data = self.train_valid_split(
|
|
train_data, valid_minutes=self.valid_minutes, random_state=42
|
|
)
|
|
|
|
self.train_data = self.prepare_traindata(train_data)
|
|
self._validation = self.prepare_mapstype(self.val_data)
|
|
|
|
test_clean = os.path.join(self.root_dir, self.files.test_clean)
|
|
test_noisy = os.path.join(self.root_dir, self.files.test_noisy)
|
|
fp = Fileprocessor.from_name(
|
|
self.name, test_clean, test_noisy, self.matching_function
|
|
)
|
|
test_data = fp.prepare_matching_dict()
|
|
self._test = self.prepare_mapstype(test_data)
|
|
|
|
def train_valid_split(
|
|
self, data, valid_minutes: float = 20, random_state: int = 42
|
|
):
|
|
|
|
valid_minutes *= 60
|
|
valid_sec_now = 0.0
|
|
valid_indices = []
|
|
all_speakers = np.unique(
|
|
[Path(file["clean"]).name.split("_")[0] for file in data]
|
|
)
|
|
possible_indices = list(range(0, len(all_speakers)))
|
|
rng = create_unique_rng(len(all_speakers))
|
|
|
|
while valid_sec_now <= valid_minutes:
|
|
speaker_index = rng.choice(possible_indices)
|
|
possible_indices.remove(speaker_index)
|
|
speaker_name = all_speakers[speaker_index]
|
|
print(f"Selected f{speaker_name} for valid")
|
|
file_indices = [
|
|
i
|
|
for i, file in enumerate(data)
|
|
if speaker_name == Path(file["clean"]).name.split("_")[0]
|
|
]
|
|
for i in file_indices:
|
|
valid_indices.append(i)
|
|
valid_sec_now += data[i]["duration"]
|
|
|
|
train_data = [
|
|
item for i, item in enumerate(data) if i not in valid_indices
|
|
]
|
|
valid_data = [item for i, item in enumerate(data) if i in valid_indices]
|
|
return train_data, valid_data
|
|
|
|
def prepare_traindata(self, data):
|
|
train_data = []
|
|
for item in data:
|
|
clean, noisy, total_dur = item.values()
|
|
num_segments = self.get_num_segments(
|
|
total_dur, self.duration, self.stride
|
|
)
|
|
samples_metadata = ({"clean": clean, "noisy": noisy}, num_segments)
|
|
train_data.append(samples_metadata)
|
|
return train_data
|
|
|
|
@staticmethod
|
|
def get_num_segments(file_duration, duration, stride):
|
|
|
|
if file_duration < duration:
|
|
num_segments = 1
|
|
else:
|
|
num_segments = math.ceil((file_duration - duration) / stride) + 1
|
|
|
|
return num_segments
|
|
|
|
def prepare_mapstype(self, data):
|
|
|
|
metadata = []
|
|
for item in data:
|
|
clean, noisy, total_dur = item.values()
|
|
if total_dur < self.duration:
|
|
metadata.append(({"clean": clean, "noisy": noisy}, 0.0))
|
|
else:
|
|
num_segments = self.get_num_segments(
|
|
total_dur, self.duration, self.duration
|
|
)
|
|
for index in range(num_segments):
|
|
start_time = index * self.duration
|
|
metadata.append(
|
|
({"clean": clean, "noisy": noisy}, start_time)
|
|
)
|
|
return metadata
|
|
|
|
def train_collatefn(self, batch):
|
|
|
|
output = {"clean": [], "noisy": []}
|
|
for item in batch:
|
|
output["clean"].append(item["clean"])
|
|
output["noisy"].append(item["noisy"])
|
|
|
|
output["clean"] = torch.stack(output["clean"], dim=0)
|
|
output["noisy"] = torch.stack(output["noisy"], dim=0)
|
|
|
|
if self.augmentations is not None:
|
|
noise = output["noisy"] - output["clean"]
|
|
output["clean"] = self.augmentations(
|
|
output["clean"], sample_rate=self.sampling_rate
|
|
)
|
|
self.augmentations.freeze_parameters()
|
|
output["noisy"] = (
|
|
self.augmentations(noise, sample_rate=self.sampling_rate)
|
|
+ output["clean"]
|
|
)
|
|
|
|
return output
|
|
|
|
@property
|
|
def generator(self):
|
|
generator = torch.Generator()
|
|
if hasattr(self, "model"):
|
|
seed = self.model.current_epoch + LARGE_NUM
|
|
else:
|
|
seed = LARGE_NUM
|
|
return generator.manual_seed(seed)
|
|
|
|
def train_dataloader(self):
|
|
return DataLoader(
|
|
TrainDataset(self),
|
|
batch_size=self.batch_size,
|
|
num_workers=self.num_workers,
|
|
generator=self.generator,
|
|
collate_fn=self.train_collatefn,
|
|
)
|
|
|
|
def val_dataloader(self):
|
|
return DataLoader(
|
|
ValidDataset(self),
|
|
batch_size=self.batch_size,
|
|
num_workers=self.num_workers,
|
|
)
|
|
|
|
def test_dataloader(self):
|
|
return DataLoader(
|
|
TestDataset(self),
|
|
batch_size=self.batch_size,
|
|
num_workers=self.num_workers,
|
|
)
|
|
|
|
|
|
class EnhancerDataset(TaskDataset):
|
|
"""
|
|
Dataset object for creating clean-noisy speech enhancement datasets
|
|
paramters:
|
|
name : str
|
|
name of the dataset
|
|
root_dir : str
|
|
root directory of the dataset containing clean/noisy folders
|
|
files : Files
|
|
dataclass containing train_clean, train_noisy, test_clean, test_noisy
|
|
folder names (refer enhancer.utils.Files dataclass)
|
|
duration : float
|
|
expected audio duration of single audio sample for training
|
|
sampling_rate : int
|
|
desired sampling rate
|
|
batch_size : int
|
|
batch size of each batch
|
|
num_workers : int
|
|
num workers to be used while training
|
|
matching_function : str
|
|
maching functions - (one_to_one,one_to_many). Default set to None.
|
|
use one_to_one mapping for datasets with one noisy file for each clean file
|
|
use one_to_many mapping for multiple noisy files for each clean file
|
|
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
name: str,
|
|
root_dir: str,
|
|
files: Files,
|
|
valid_minutes=5.0,
|
|
duration=1.0,
|
|
stride=None,
|
|
sampling_rate=48000,
|
|
matching_function=None,
|
|
batch_size=32,
|
|
num_workers: Optional[int] = None,
|
|
augmentations: Optional[Compose] = None,
|
|
):
|
|
|
|
super().__init__(
|
|
name=name,
|
|
root_dir=root_dir,
|
|
files=files,
|
|
valid_minutes=valid_minutes,
|
|
sampling_rate=sampling_rate,
|
|
duration=duration,
|
|
matching_function=matching_function,
|
|
batch_size=batch_size,
|
|
num_workers=num_workers,
|
|
augmentations=augmentations,
|
|
)
|
|
|
|
self.sampling_rate = sampling_rate
|
|
self.files = files
|
|
self.duration = max(1.0, duration)
|
|
self.audio = Audio(self.sampling_rate, mono=True, return_tensor=True)
|
|
self.stride = stride or duration
|
|
|
|
def setup(self, stage: Optional[str] = None):
|
|
|
|
super().setup(stage=stage)
|
|
|
|
def train__getitem__(self, idx):
|
|
|
|
for filedict, num_samples in self.train_data:
|
|
if idx >= num_samples:
|
|
idx -= num_samples
|
|
continue
|
|
else:
|
|
start = 0
|
|
if self.duration is not None:
|
|
start = idx * self.stride
|
|
return self.prepare_segment(filedict, start)
|
|
|
|
def val__getitem__(self, idx):
|
|
return self.prepare_segment(*self._validation[idx])
|
|
|
|
def test__getitem__(self, idx):
|
|
return self.prepare_segment(*self._test[idx])
|
|
|
|
def prepare_segment(self, file_dict: dict, start_time: float):
|
|
print(file_dict["clean"].split("/")[-1])
|
|
clean_segment = self.audio(
|
|
file_dict["clean"], offset=start_time, duration=self.duration
|
|
)
|
|
noisy_segment = self.audio(
|
|
file_dict["noisy"], offset=start_time, duration=self.duration
|
|
)
|
|
clean_segment = F.pad(
|
|
clean_segment,
|
|
(
|
|
0,
|
|
int(
|
|
self.duration * self.sampling_rate - clean_segment.shape[-1]
|
|
),
|
|
),
|
|
)
|
|
noisy_segment = F.pad(
|
|
noisy_segment,
|
|
(
|
|
0,
|
|
int(
|
|
self.duration * self.sampling_rate - noisy_segment.shape[-1]
|
|
),
|
|
),
|
|
)
|
|
return {
|
|
"clean": clean_segment,
|
|
"noisy": noisy_segment,
|
|
}
|
|
|
|
def train__len__(self):
|
|
_, num_examples = list(zip(*self.train_data))
|
|
return sum(num_examples)
|
|
|
|
def val__len__(self):
|
|
return len(self._validation)
|
|
|
|
def test__len__(self):
|
|
return len(self._test)
|